AI Engineer (Generative AI / LLM)

Job Summary

We are seeking an experienced AI Engineer with 12+ years of software engineering experience and strong expertise in Generative AI, Large Language Models (LLMs), Machine Learning, and AI application development. The ideal candidate will design, build, deploy, and optimize enterprise-grade AI solutions using modern LLM frameworks, cloud AI services, vector databases, and MLOps best practices.

Required Skills

• 12+ years of software development experience.
• 4+ years of hands-on experience with AI/ML and Generative AI.
• Strong programming skills in Python.
• Experience with Large Language Models (GPT-4, Llama, Claude, Gemini, Mistral, Falcon).
• Expertise in Prompt Engineering, RAG (Retrieval-Augmented Generation), AI Agents, and Fine-Tuning.
• Hands-on experience with LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, Semantic Kernel, Haystack.
• Experience with OpenAI API, Azure OpenAI, AWS Bedrock, Google Vertex AI.
• Knowledge of Vector Databases:
  • Pinecone
  • Weaviate
  • ChromaDB
  • Milvus
  • FAISS
• Experience with Machine Learning frameworks:
  • PyTorch
  • TensorFlow
  • Scikit-learn
• Strong understanding of:
  • NLP
  • Transformers
  • Embeddings
  • Tokenization
  • Semantic Search
• Experience building REST APIs using:
  • FastAPI
  • Flask
• Strong SQL and NoSQL database experience:
  • PostgreSQL
  • MongoDB
  • Redis
  • Elasticsearch
• Experience with Docker, Kubernetes, and CI/CD pipelines.
• Strong cloud experience in AWS, Azure, or Google Cloud Platform.
• Experience with Git, GitHub, Azure DevOps, or Jenkins.

Responsibilities

• Design and develop enterprise-scale AI and Generative AI applications.
• Build LLM-powered chatbots, virtual assistants, and intelligent automation solutions.
• Develop Retrieval-Augmented Generation (RAG) pipelines using vector databases.
• Build AI Agents and multi-agent workflows.
• Fine-tune and optimize open-source and commercial LLMs.
• Integrate AI services with enterprise applications using REST APIs.
• Develop scalable inference pipelines and model-serving solutions.
• Implement prompt engineering strategies to improve model performance.
• Build evaluation frameworks for AI model quality, accuracy, and safety.
• Optimize latency, throughput, and inference costs.
• Deploy AI workloads on AWS, Azure, or Google Cloud Platform.
• Implement MLOps best practices for model deployment, monitoring, and versioning.
• Collaborate with data engineers, architects, product owners, and business stakeholders.
• Ensure AI security, governance, compliance, and responsible AI practices.

Preferred Qualifications

• Experience with AI Agents and autonomous workflows.
• Experience with GraphRAG and Knowledge Graphs.
• Knowledge of Hugging Face ecosystem.
• Experience with MLflow, Kubeflow, or SageMaker.
• Exposure to Computer Vision or Speech AI is a plus.
• Experience with Databricks, Snowflake, or Spark.
• Azure OpenAI, AWS Bedrock, or Vertex AI certifications are preferred.

Nice to Have

• Multi-modal AI (Text, Image, Audio, Video)
• Agentic AI
• MCP (Model Context Protocol)
• AI Security & Guardrails
• Prompt Optimization
• Reinforcement Learning from Human Feedback (RLHF)
• LLM Evaluation Frameworks
• Synthetic Data Generation
• AI Governance