AI/ML Engineer

Job Summary

We are looking for an AI/ML Engineer – Agentic AI with strong experience in Python, LLMs, LangGraph/LangChain, RAG, multi-agent systems, and prompt engineering.

The role focuses on building and deploying autonomous AI agents, enterprise copilots, and intelligent workflows using Azure/AWS, Docker, Kubernetes, and MLOps/AgentOps, with an emphasis on scalability, security, observability, and responsible AI.

Key Technical Skills

1. Core Programming & Systems

• Expert-level Python for ML, orchestration, and agent logic
• Strong understanding of async programming, concurrency, and task scheduling2. Agentic AI
• Autonomous AI agents for multi-step reasoning, planning, task orchestration, and decision-making
• ReAct, Plan & Execute, Reflexive agents
• Hierarchical and multi-agent systems
• Tool-augmented and function-calling agents
• Stateful/stateless agents and memory management3. Large Language Models
• Hands-on experience with OpenAI, Azure OpenAI, Anthropic, or open-source LLMs
• Prompt engineering, reasoning, planning, critique, and tool-use techniques
• Few-shot and zero-shot prompting
• Model selection based on latency, cost, and context length
• Fine-tuning/LoRA knowledge is a plus4. Agent Frameworks & Tooling
• LangGraph / LangChain
• Semantic Kernel
• AutoGen, CrewAI, or similar frameworks
• Custom agent orchestration
• Tool abstraction, execution safety, retries, and timeouts5. RAG, Memory & Knowledge Augmentation
• Short-term and long-term agent memory
• RAG implementation
• Vector databases such as FAISS, Pinecone, Azure AI Search, etc.
• Embeddings, chunking, and context management
• Knowledge graph/hybrid memory is a plus6. Planning, Reasoning & Control
• Task planning and re-planning
• Constraint-based execution
• Feedback and self-correction loops
• Guardrails, action validation, failure detection, and recovery7. MLOps / AgentOps
• Production deployment of AI agents
• Agent observability, tracing, logging, and monitoring
• Model and prompt versioning
• CI/CD for AI/agent systems
• Docker, Kubernetes, and Azure/AWS deployments8. Evaluation & Testing
• Agent evaluation frameworks
• Task success, cost, latency, reliability, and safety evaluation
• Hallucination detection
• A/B testing of prompts, tools, and agent strategies9. Security & Responsible AI
• Secure tool execution and privilege control
• Prompt injection and jailbreak mitigation
• Data privacy and memory isolation
• Human-in-the-loop escalation
• Explainability and responsible AI practices10. Data & Integration
• Enterprise system, API, CRM, ERP, database, and SaaS integrations
• SQL / NoSQL
• Event-driven systems and message queues are a plus11. Cloud & Platform
• Strong experience in at least one cloud platform: Azure, AWS, or GCP
• Managed AI services
• IAM, secrets management, and security
• LLM cost optimization12. Nice to Have
• Multi-agent collaboration
• Enterprise copilots and autonomous workflows
• Human-AI collaboration patterns
• Reinforcement learning for agent optimization