GenAI Engineer

Job Title: Senior Generative AI Engineer
Experience: 12+ Years
Location: [Remote/Hybrid/Onsite]
Employment Type: W2

Position Overview

We are seeking a highly experienced Senior Generative AI Engineer with 12+ years of overall software engineering experience and deep expertise in designing, developing, and deploying enterprise-scale AI/ML and Generative AI solutions. The ideal candidate will have strong hands-on experience with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, prompt engineering, model fine-tuning, vector databases, and cloud-native AI platforms.

This role requires a combination of software engineering excellence, AI/ML expertise, solution architecture skills, and the ability to lead GenAI initiatives from concept to production.

Key Responsibilities

Generative AI Development

• Design, develop, and deploy enterprise-grade Generative AI applications using state-of-the-art LLMs.
• Build and optimize RAG (Retrieval-Augmented Generation) architectures for knowledge-intensive applications.
• Develop AI-powered chatbots, virtual assistants, copilots, and intelligent automation solutions.
• Design and implement multi-agent AI systems and agentic workflows.
• Perform prompt engineering, model evaluation, and response optimization.
• Fine-tune and customize foundation models for domain-specific use cases.

AI/ML Engineering

• Build end-to-end machine learning and GenAI pipelines.
• Develop model training, validation, deployment, and monitoring frameworks.
• Implement MLOps and LLMOps best practices.
• Create automated evaluation systems for LLM performance, safety, and accuracy.
• Work with structured, semi-structured, and unstructured data sources.

Architecture & Solution Design

• Architect scalable AI platforms capable of supporting enterprise workloads.
• Design microservices-based AI applications using cloud-native technologies.
• Integrate GenAI solutions with enterprise applications, APIs, databases, and business workflows.
• Establish AI governance, security, and compliance standards.

Leadership & Collaboration

• Lead technical design discussions and architecture reviews.
• Mentor AI engineers, data scientists, and software developers.
• Collaborate with business stakeholders to identify AI opportunities and define solution roadmaps.
• Drive innovation and evaluate emerging AI technologies and frameworks.

Required Qualifications

Experience

• 12+ years of overall software engineering experience.
• 5+ years of AI/ML development experience.
• 3+ years of hands-on Generative AI implementation experience.
• Proven experience delivering production-grade GenAI solutions at enterprise scale.

Programming & Development

• Expert-level proficiency in:
  • Python
  • REST APIs
  • Microservices Architecture
  • Object-Oriented Design
• Experience with:
  • FastAPI
  • Flask
  • Async Programming
  • Docker
  • Kubernetes

Generative AI Technologies

Strong hands-on experience with:

• OpenAI GPT Models
• Claude
• Llama Models
• Gemini
• Mistral
• Prompt Engineering
• Function Calling
• AI Agents
• Multi-Agent Systems
• Tool Usage Frameworks

GenAI Frameworks

Experience with:

• LangChain
• LangGraph
• LlamaIndex
• CrewAI
• AutoGen
• Semantic Kernel

Retrieval & Vector Search

Experience implementing:

• RAG Architectures
• Hybrid Search
• Semantic Search
• Knowledge Graphs

Vector databases:

• Pinecone
• Weaviate
• ChromaDB
• FAISS
• Milvus
• Elasticsearch/OpenSearch

AI/ML Frameworks

Strong experience with:

• PyTorch
• TensorFlow
• Scikit-learn
• Hugging Face Transformers
• PEFT
• LoRA
• Fine-Tuning Techniques

MLOps / LLMOps

Hands-on experience with:

• MLflow
• Kubeflow
• SageMaker
• Model Monitoring
• Model Registry
• CI/CD for AI Systems
• Experiment Tracking

Cloud Platforms

Experience with at least one:

• Amazon Web Services (AWS)
• Microsoft Azure
• Google Cloud

Services such as:

• AI/ML Services
• Vector Search Services
• Serverless Computing
• Container Platforms

Databases

Experience with:

• PostgreSQL
• MySQL
• MongoDB
• Redis
• Neo4j
• Vector Databases

Preferred Qualifications

• Experience building AI solutions in Financial Services, Healthcare, Insurance, Retail, or Technology domains.
• Experience with Responsible AI and AI Governance frameworks.
• Knowledge of AI security, model explainability, and compliance requirements.
• Experience with Knowledge Graphs and Graph Databases.
• Experience integrating GenAI with enterprise platforms such as Salesforce, ServiceNow, SAP, or Workday.
• Exposure to multimodal AI applications involving text, images, audio, and video.

Education

• Bachelor''s or Master''s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or related field.
• Advanced AI/ML certifications preferred.

Nice-to-Have Certifications

• AWS Certified Machine Learning Specialty
• Azure AI Engineer Associate
• Google Professional Machine Learning Engineer
• Databricks Machine Learning Professional
• Generative AI Professional Certifications

Key Skills Summary

Generative AI: LLMs, GPT, Claude, Gemini, Llama, RAG, AI Agents, Prompt Engineering, Fine-Tuning

Frameworks: LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen

Programming: Python, FastAPI, Flask, REST APIs, Microservices

ML/AI: PyTorch, TensorFlow, Scikit-learn, Hugging Face

LLMOps/MLOps: MLflow, Kubeflow, CI/CD, Monitoring

Cloud: AWS, Azure, Google Cloud Platform

Databases: PostgreSQL, MongoDB, Redis, Neo4j, Pinecone, Weaviate, FAISS

Architecture: Distributed Systems, Cloud-Native Design, Enterprise AI Platforms

This role is ideal for a senior technologist who can lead the design and implementation of cutting-edge Generative AI solutions while driving enterprise AI transformation initiatives.