Greetings from SRINAV INC.
Kindly find the below requirement and share your interest.
Position : Sr AI Design / Implementation Engineer
Location : Plano, TX, (Hybrid)
Duration : Long term
We need strong candidates who have developed LLM s, Designed workflows and AI tools deployment knowledge.
Join a horizontal engineering team supporting 600+ application teams on a mission to raise engineering maturity by driving standards, guidelines, platform capabilities, and large-scale technical debt remediation. You will build advanced agentic AI workflows to automatically analyze codebases, detect tech debt, and generate high-quality fixes from vulnerability patches to dependency and language upgrades. This is a hands-on, high-impact role shaping the future of automated software modernization.
Key Responsibilities:
Design, develop, and maintain LLM-powered multi-agent workflows for code analysis, remediation proposals, and safe patch generation.
Implement agentic patterns including planning/execution loops, dynamic tool orchestration, sandboxing, guardrails, and failure recovery.
Build scalable automation systems for technical debt remediation: language/runtime upgrades, vulnerability patching, dependency modernization, and config drift correction.
Partner with Dev Experience and Platform teams to define engineering guidelines and reusable standards across the organization.
Architect and optimize Retrieval-Augmented Generation (RAG) pipelines, managing chunking, embeddings, hybrid search, reranking, and retrieval policies.
Develop robust evaluation frameworks for LLMs, RAG, and agent workflows, including offline datasets, validation metrics, statistical testing, and A/B tests.
Contribute to backend systems using Python, distributed systems, microservices, PostgreSQL, DBT, vector databases, caching, streaming, and queueing.
Build CI/CD pipelines, observability dashboards, and perform performance analysis on model, retrieval, and network layers.
Collaborate cross-functionally with product, platform, and security to move prototypes to production-grade services.
Communicate clearly with stakeholders, write technical documentation, and mentor junior engineers.
MUST HAVE Qualifications:
Platform runs on AWS and AWS knowledge is must.
5+ years experience building production-grade systems with end-to-end ownership.
Expertise in Python programming, software engineering best practices, testing strategies, CI/CD, and system design.
Hands-on experience shipping LLM-powered features such as autonomous workflows or function calling with measurable impact on reliability or latency.
Deep knowledge of multi-agent architectures including planners, executors, and tool routing.
Strong understanding of RAG systems: chunking, embeddings, vector/hybrid search, and retrieval policies.
Experience evaluating LLMs and agent workflows incorporating statistical reasoning and validation.
Proficiency with AWS (Lambda, ECS/EKS, S3, API Gateway, EC2, IAM) and Infrastructure-as-Code for cloud resource automation and deployment.
Experience with observability tools (Datadog, logging, tracing, metrics).
Familiarity with PostgreSQL, DBT, data modeling, schema evolution, and performance tuning.
Knowledge of vector databases like Pinecone or pgvector.
Experience building or optimizing CI/CD pipelines (GitHub Actions or similar).
Proven track record in application modernization, dependency management, and technical debt reduction.
Ability to rapidly prototype, validate, and transition solutions to production systems.
Preferred Skills:
Experience designing agent infrastructure with sandboxing, tool isolation, and fail-safe execution.
Background in large-scale platform engineering or developer experience tooling.
Understanding of security, compliance, and privacy for enterprise AI systems.
Strong architectural communication ability, including RFC writing and diagramming.
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