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Senior AI Engineer/ Lead AI Engineer(Local to GA Only)

Trigint Solutions

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Easy Apply

Third Party, Contract

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AI Engineer Lead

CipherMax LLC

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Urgent Opening for Priority | Physical Design Engineer - Lead

Apetan Consulting

Remote • Yesterday

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Machine Learning Lead Engineer, Machine Learning (ML) Required, Work From Home - G

Next Step Systems

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Easy Apply

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$160,000 - $190,000

Oracle Integration Cloud (OIC) Engineer / Lead

Econosoft

Remote • 17d ago

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Senior Palantir Data Engineer (Lead)

Unissant, Inc.

Remote or Herndon, Virginia • 23d ago

Easy Apply

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Depends on Experience

Mainframe Reverse Engineer Lead

RAVIN IT SOLUTIONS, Inc

Remote • 18d ago

Easy Apply

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Depends on Experience

AI Engineer / Lead / Architect | 100% Remote Role | 3Roles

Pioneer Corporate Services Inc

Remote • 29d ago

Easy Apply

Third Party, Contract

Depends on Experience

Engineer Lead

Elevance Health

Atlanta, Georgia • Today

Full-time

Engineer Lead Senior

Elevance Health

Atlanta, Georgia • Today

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AI Solutions Engineer Lead Senior

Elevance Health

Atlanta, Georgia • Today

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Forward Deployed Process Engineer Lead - R37

Remote • Today

Full-time

USD 152,163.00 - 289,353.75 per year

Rust Software Engineer Lead, Distributed Ledger

FIS

Remote or New York, New York • Today

Full-time

USD 133,980.00 - 227,770.00 per year

Lead Engineer - Generator Product Service Engineering

GE Vernova

Atlanta, Georgia • Today

Full-time

USD 98,400.00 - 164,000.00 per year

Lead Engineer - Accessories, Product Service Americas

GE Vernova

Atlanta, Georgia • Today

Full-time

USD 98,400.00 - 164,000.00 per year

Lead Engineer, Platform Engineering & Reliability

Nasdaq

Atlanta, Georgia • Today

Full-time

Compensation information provided in the description

Software Engineer - Salesforce Technical Lead

Truist

Atlanta, Georgia • Today

Full-time

Systems Engineer, Lead Consultant Identity & Authentication - Remote

Allstate Insurance Company

Remote or Illinois • Today

Full-time

USD 98,100.00 - 167,850.00 per year

Systems Engineer, Lead Consultant Identity & Authentication - Remote

Allstate Insurance Company

Remote or Chicago, Illinois • Today

Full-time

USD 98,100.00 - 167,850.00 per year

IT Security Engineer IV

Russell, Tobin & Associates

Atlanta, Georgia • 3d ago

Easy Apply

Contract

$100 - $105.86

Lead Software Engineer - Azure & .NET

Randstad Digital

Remote or Omaha, Nebraska • Today

Contract

USD70 - USD100

Vice President, Lead Software Engineer

Morgan Stanley

Alpharetta, Georgia • Today

Full-time

USD 175,000.00 - 175,000.00 per year

Lead Software Engineer, AI Email Applications, Artificial Intelligence (AI) Required - Work From Home - M

Next Step Systems

Remote • Today

Easy Apply

Full-time

$175,000 - $195,000

Lead Software Engineer/Architect

Newell Brands

Atlanta, Georgia • Today

Full-time

Lead Engineer, Infrastructure Solutions Architecture

Intercontinental Exchange, Inc

Atlanta, Georgia • Today

Full-time

USD 133,900.00 - 154,500.00 per year

**Lead Engineer Agentic AI** - W2 Role

Kellton

Remote • 16d ago

Easy Apply

Contract, Third Party

Depends on Experience

Lead AI Security Engineer - Senior Manager

Atlanta, Georgia • Today

Full-time

USD 150,700.00 - 251,200.00 per year

Sr. BI Lead Databricks Engineer

Stefanini

Remote or Humble, Texas • Today

Easy Apply

Contract

$80 - $85 /hr

Lead Mainframe Storage Engineer

Morgan Stanley

Alpharetta, Georgia • Today

Full-time

USD 110,000.00 - 165,000.00 per year

Sr. Lead Software Engineer

JPMorgan Chase & Co.

Atlanta, Georgia • Today

Full-time

Job Details

Skills

conversational AI based product on AWS

Summary

Position 1: Senior AI Engineer

JD:-

Senior AI Engineer Conversational & Agentic AI

Job Description:

Experience	5 10 years overall, 3+ in GenAI/Agentic AI
Focus	Build conversational AI based product on AWS
Location	Atlanta, GA (USA)

About the role

You will work closely with a client team in Atlanta to build a conversational AI product on AWS in a fast-paced environment. You own major components end to end, turn the architecture into production-quality code, and help the team move from concept to a production-ready release on an accelerated timeline. You are comfortable demoing to client stakeholders and leading technical work for less experienced engineers.

What you will do

Build the core of the product conversational AI, RAG and agents on AWS Bedrock/Bedrock AgentCore working closely with the AI Architect.
Implement chatbot features: multi-turn conversation flows, session memory, tool calling, streaming responses and human handoff.
Build RAG pipelines: document ingestion, chunking, embeddings, hybrid search, reranking, metadata filtering and source citation.
Develop MCP servers that expose enterprise APIs and data as governed tools, and integrate agents with each other using A2A.
Drive rapid, iterative delivery: ship a working MVP quickly, then harden, optimise and load test it for production.
Engineer for scale: tune latency, throughput and cost so the chatbot holds up under thousands of concurrent users.
Build evaluation suites for answer quality, groundedness and regression testing of prompts and models.
Ship through CI/CD with infrastructure as code, logging, tracing, alerting and cost monitoring.
Work as part of the client team: estimate and break down work, join weekly demos and explain technical trade-offs to stakeholders.
Review code, mentor engineers and contribute to runbooks and technical documentation for handover.

Must have

Enterprise delivery: built and shipped at least two production-grade GenAI or conversational AI applications, with ownership of significant components, including one delivered on a tight timeline.
Embedded, fast-paced delivery: worked as part of consulting or client teams; comfortable with ambiguous scope, weekly demos and stakeholder exposure.
Hands-on engineering: strong production Python and REST APIs
Conversational AI: built production chatbots or virtual assistants with multi-turn context, intent handling, session memory, human handoff and multichannel delivery (web, mobile)
Chatbot scale: worked on chatbots running at high concurrency in production. Candidates should state peak concurrent users, p95 latency and their part in scaling it.
Scale engineering: response streaming, semantic and response caching, retries and rate-limit handling, provisioned throughput, autoscaling and load testing.
RAG: implemented retrieval pipelines with vector stores (OpenSearch, pgvector, Aurora, Pinecone or similar) and measured retrieval quality and groundedness.
AWS Bedrock (essential): model invocation and selection, Knowledge Bases, Guardrails, Agents and model evaluation.
Bedrock AgentCore (essential): hands-on with Runtime, Memory, Gateway, Identity and Observability to deploy and operate agents.
MCP: built MCP servers and clients, including authentication and authorisation for tools.
A2A and multi-agent: built multi-agent workflows using A2A and frameworks such as Strands Agents, LangGraph or CrewAI.
AWS foundations: Lambda, API Gateway, ECS or EKS, DynamoDB, S3, IAM, VPC networking and CloudWatch.
Responsible AI: guardrails, hallucination control, prompt-injection defence and PII handling in regulated environments.
LLM observability and evaluation: Langfuse, Ragas, Bedrock evaluations or similar tools to trace, monitor and evaluate LLM applications.
Location: based in or able to relocate to Atlanta; US work authorisation required.

Nice to have

Voice AI with Amazon Connect, Lex or speech models.
Equivalent platforms on Azure OpenAI, Vertex AI or Databricks Mosaic AI.
Fine-tuning, distillation or small-model deployment for cost and latency.
Product mindset: conversational UX, user feedback loops and A/B testing of prompts or flows.
Front-end chat UI experience (React).
AWS Certification on AI/GenAI
Domain experience in Finance

Position 2: Lead AI Engineer

Job Description

Experience	10-15 years overall, 5+ in GenAI/Agentic AI
Focus	Design and Build conversational AI based product on AWS
Location	Atlanta, GA (USA)

About the role

You will work closely with a client team in Atlanta to design and build a conversational AI product on AWS in a fast-paced environment. You will lead requirement gathering with business and technical stakeholders, own the end-to-end architecture, make fast technical calls, and stay hands-on so the team moves from concept to a production-ready release on an accelerated timeline. You are equally comfortable at a whiteboard with client leadership and in a code review with engineers.

What you will do

Lead requirement gathering: run discovery workshops with business and technical stakeholders, map user journeys and conversation flows, capture functional and non-functional requirements, and turn them into a prioritised backlog with clear acceptance criteria.
Define the target architecture for the product early and decisively conversational AI, RAG and agents on AWS Bedrock/Bedrock AgentCore.
Design chatbots and assistants that can serve thousands of concurrent users with predictable latency, cost and uptime.
Architect agent ecosystems using MCP for tool and data access and A2A for agent-to-agent collaboration.
Drive rapid, iterative delivery: get to a working MVP quickly, then harden, load test and take the product to production readiness.
Build hands-on alongside the team: agent code, prompts, retrieval pipelines, integrations with data platforms and enterprise systems, and infrastructure as code.
Set standards for LLMOps: evaluation, prompt and model versioning, observability, guardrails and cost governance.
Own non-functional design: security, identity and access, PII handling, compliance, resilience and disaster recovery.
Work as part of the client team: shape scope and trade-offs with stakeholders, run weekly demos and present architecture and progress to client leadership.
Run design reviews, mentor engineers and hand over a documented, operable platform (runbooks, architecture decisions, cost model) at the end of the engagement.

Must have

Enterprise delivery: architected and delivered multiple production-grade GenAI or conversational AI products end to end, including at least one taken from concept to production on a tight timeline.
Embedded, fast-paced delivery: worked as part of consulting or client teams; comfortable with ambiguous scope, weekly demos and senior stakeholder exposure.
Requirements and discovery: led discovery and requirement workshops for AI products; able to translate business goals into use cases, conversation flows, user stories and measurable success criteria.
Hands-on builder: writes production Python and infrastructure as code (CDK, Terraform or CloudFormation) not a diagram-only architect.
Conversational AI: production chatbots or virtual assistants with multi-turn context, intent handling, session memory, human handoff and multichannel delivery (web, mobile, messaging, contact centre).
Chatbot scale: designed systems running at high concurrency in production. Candidates should state peak concurrent users, p95 latency and daily conversation volume they handled.
Scale engineering: response streaming, provisioned throughput and quota planning, semantic and response caching, load testing, autoscaling and graceful degradation under model rate limits.
RAG: retrieval pipeline design chunking, embeddings, hybrid search, reranking, metadata filtering, vector stores (OpenSearch, pgvector, Aurora, Pinecone or similar) and groundedness evaluation.
AWS Bedrock (essential): foundation model selection, Knowledge Bases, Guardrails, Agents, model evaluation and cost optimisation.
Bedrock AgentCore (essential): Runtime, Memory, Gateway, Identity and Observability for deploying and operating agents securely at scale.
MCP: designed MCP servers and clients that expose enterprise APIs and data as governed tools, including authentication and authorisation.
A2A and multi-agent: orchestration patterns (supervisor, hierarchical, peer-to-peer) using A2A and frameworks such as Strands Agents, LangGraph or CrewAI.
Foundations: strong AWS architecture (serverless, containers, networking, IAM, security), distributed systems and API design; Python hands-on.
Responsible AI: guardrails, hallucination control, prompt-injection defence, auditability and data privacy in regulated environments.
LLM observability and evaluation: Langfuse, Ragas, Bedrock evaluations or similar tools to trace, monitor and evaluate LLM applications.
Location: based in or able to relocate to Atlanta; US work authorisation required.

Nice to have

Voice AI with Amazon Connect, Lex or speech models.
Equivalent platforms on Azure OpenAI, Vertex AI or Databricks Mosaic AI.
Fine-tuning, distillation or small-model deployment for cost and latency.
Product mindset: conversational UX design, user feedback loops and A/B testing of prompts or flows.
AWS Certification on AI/GenAI
Domain experience in Finance

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: 90999304
Position Id: 9101730
Posted 8 days ago

Fitment

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Hello Professionals, Hope this mail finds you well! We have an immediate contract opportunity with one of our clients. Please review the details below and if you are comfortable with JD please share your updated resume at Referrals are also appreciated! AI Engineer Quality Engineering AI CoE Location: Atlanta, GA- Onsite Rate: $50/hr. On W2 Only - Please share profile only if you are okay with given rate Experience Required: 8+ Years Visa Eligibility : OPT-EAD, STEM-OPT, H1B, -EAD, E3, TN onl

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Skills

conversational AI based product on AWS

Summary

Position 1: Senior AI Engineer

JD:-

Senior AI Engineer Conversational & Agentic AI

Job Description:

Experience	5 10 years overall, 3+ in GenAI/Agentic AI
Focus	Build conversational AI based product on AWS
Location	Atlanta, GA (USA)

About the role

What you will do

Build the core of the product conversational AI, RAG and agents on AWS Bedrock/Bedrock AgentCore working closely with the AI Architect.
Implement chatbot features: multi-turn conversation flows, session memory, tool calling, streaming responses and human handoff.
Build RAG pipelines: document ingestion, chunking, embeddings, hybrid search, reranking, metadata filtering and source citation.
Develop MCP servers that expose enterprise APIs and data as governed tools, and integrate agents with each other using A2A.
Drive rapid, iterative delivery: ship a working MVP quickly, then harden, optimise and load test it for production.
Engineer for scale: tune latency, throughput and cost so the chatbot holds up under thousands of concurrent users.
Build evaluation suites for answer quality, groundedness and regression testing of prompts and models.
Ship through CI/CD with infrastructure as code, logging, tracing, alerting and cost monitoring.
Work as part of the client team: estimate and break down work, join weekly demos and explain technical trade-offs to stakeholders.
Review code, mentor engineers and contribute to runbooks and technical documentation for handover.

Must have

Enterprise delivery: built and shipped at least two production-grade GenAI or conversational AI applications, with ownership of significant components, including one delivered on a tight timeline.
Embedded, fast-paced delivery: worked as part of consulting or client teams; comfortable with ambiguous scope, weekly demos and stakeholder exposure.
Hands-on engineering: strong production Python and REST APIs
Conversational AI: built production chatbots or virtual assistants with multi-turn context, intent handling, session memory, human handoff and multichannel delivery (web, mobile)
Chatbot scale: worked on chatbots running at high concurrency in production. Candidates should state peak concurrent users, p95 latency and their part in scaling it.
Scale engineering: response streaming, semantic and response caching, retries and rate-limit handling, provisioned throughput, autoscaling and load testing.
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AWS foundations: Lambda, API Gateway, ECS or EKS, DynamoDB, S3, IAM, VPC networking and CloudWatch.
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LLM observability and evaluation: Langfuse, Ragas, Bedrock evaluations or similar tools to trace, monitor and evaluate LLM applications.
Location: based in or able to relocate to Atlanta; US work authorisation required.

Nice to have

Voice AI with Amazon Connect, Lex or speech models.
Equivalent platforms on Azure OpenAI, Vertex AI or Databricks Mosaic AI.
Fine-tuning, distillation or small-model deployment for cost and latency.
Product mindset: conversational UX, user feedback loops and A/B testing of prompts or flows.
Front-end chat UI experience (React).
AWS Certification on AI/GenAI
Domain experience in Finance

Position 2: Lead AI Engineer

Job Description

Experience	10-15 years overall, 5+ in GenAI/Agentic AI
Focus	Design and Build conversational AI based product on AWS
Location	Atlanta, GA (USA)

About the role

What you will do

Lead requirement gathering: run discovery workshops with business and technical stakeholders, map user journeys and conversation flows, capture functional and non-functional requirements, and turn them into a prioritised backlog with clear acceptance criteria.
Define the target architecture for the product early and decisively conversational AI, RAG and agents on AWS Bedrock/Bedrock AgentCore.
Design chatbots and assistants that can serve thousands of concurrent users with predictable latency, cost and uptime.
Architect agent ecosystems using MCP for tool and data access and A2A for agent-to-agent collaboration.
Drive rapid, iterative delivery: get to a working MVP quickly, then harden, load test and take the product to production readiness.
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Set standards for LLMOps: evaluation, prompt and model versioning, observability, guardrails and cost governance.
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Work as part of the client team: shape scope and trade-offs with stakeholders, run weekly demos and present architecture and progress to client leadership.
Run design reviews, mentor engineers and hand over a documented, operable platform (runbooks, architecture decisions, cost model) at the end of the engagement.

Must have

Enterprise delivery: architected and delivered multiple production-grade GenAI or conversational AI products end to end, including at least one taken from concept to production on a tight timeline.
Embedded, fast-paced delivery: worked as part of consulting or client teams; comfortable with ambiguous scope, weekly demos and senior stakeholder exposure.
Requirements and discovery: led discovery and requirement workshops for AI products; able to translate business goals into use cases, conversation flows, user stories and measurable success criteria.
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Chatbot scale: designed systems running at high concurrency in production. Candidates should state peak concurrent users, p95 latency and daily conversation volume they handled.
Scale engineering: response streaming, provisioned throughput and quota planning, semantic and response caching, load testing, autoscaling and graceful degradation under model rate limits.
RAG: retrieval pipeline design chunking, embeddings, hybrid search, reranking, metadata filtering, vector stores (OpenSearch, pgvector, Aurora, Pinecone or similar) and groundedness evaluation.
AWS Bedrock (essential): foundation model selection, Knowledge Bases, Guardrails, Agents, model evaluation and cost optimisation.
Bedrock AgentCore (essential): Runtime, Memory, Gateway, Identity and Observability for deploying and operating agents securely at scale.
MCP: designed MCP servers and clients that expose enterprise APIs and data as governed tools, including authentication and authorisation.
A2A and multi-agent: orchestration patterns (supervisor, hierarchical, peer-to-peer) using A2A and frameworks such as Strands Agents, LangGraph or CrewAI.
Foundations: strong AWS architecture (serverless, containers, networking, IAM, security), distributed systems and API design; Python hands-on.
Responsible AI: guardrails, hallucination control, prompt-injection defence, auditability and data privacy in regulated environments.
LLM observability and evaluation: Langfuse, Ragas, Bedrock evaluations or similar tools to trace, monitor and evaluate LLM applications.
Location: based in or able to relocate to Atlanta; US work authorisation required.

Nice to have

Voice AI with Amazon Connect, Lex or speech models.
Equivalent platforms on Azure OpenAI, Vertex AI or Databricks Mosaic AI.
Fine-tuning, distillation or small-model deployment for cost and latency.
Product mindset: conversational UX design, user feedback loops and A/B testing of prompts or flows.
AWS Certification on AI/GenAI
Domain experience in Finance

Dice Id: 90999304
Position Id: 9101730
Posted 8 days ago

Fitment

Dice Job Match Score™

To see how well you match this job, please log in or create an account.

Once logged in, be sure to complete your profile to get the most accurate match score.

Create job alert

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