AIML Engineer - Agentic AI
Job Title: AI/ML Engineer – Agentic AI
Technical Skill Set
1. Core Programming Skills
- Strong expertise in Python for machine learning, orchestration, and agent development.
- Knowledge of asynchronous programming, concurrency, and task scheduling.2. Agentic AI
- Experience designing autonomous AI agents for multi-step reasoning, planning, and task orchestration.
- Knowledge of ReAct, Plan-and-Execute, and multi-agent architectures.
- Experience building tool-augmented and function-calling agents.
- Understanding of agent memory management and stateful workflows.3. Large Language Models (LLMs)
- Hands-on experience with OpenAI, Azure OpenAI, Anthropic, or open-source LLMs.
- Strong prompt engineering skills, including few-shot and zero-shot prompting.
- Understanding of model selection, latency, cost, and context management.
- Knowledge of fine-tuning and LoRA is an advantage.4. Agent Frameworks
- Hands-on experience with LangGraph and LangChain.
- Familiarity with Semantic Kernel, AutoGen, CrewAI, or similar frameworks.
- Ability to develop custom agent orchestration workflows.5. RAG, Memory, and Knowledge Management
- Experience implementing Retrieval-Augmented Generation (RAG).
- Knowledge of vector databases such as FAISS, Pinecone, or Azure AI Search.
- Experience with embeddings, document chunking, and context management.
- Knowledge of knowledge graphs or hybrid memory systems is an advantage.6. Planning and Reasoning
- Experience implementing task planning, replanning, and self-correction workflows.
- Knowledge of guardrails, action validation, failure detection, and recovery mechanisms.7. MLOps and AgentOps
- Experience deploying AI agents in production environments.
- Knowledge of agent tracing, logging, monitoring, and prompt/model versioning.
- Experience with CI/CD, Docker, Kubernetes, or serverless deployments.8. Evaluation and Testing
- Ability to evaluate agent performance based on accuracy, task success, latency, cost, and reliability.
- Experience with testing frameworks, simulation environments, and prompt evaluation.9. Security and Responsible AI
- Understanding of prompt injection, jailbreak prevention, and secure tool execution.
- Knowledge of data privacy, access control, and responsible AI practices.
- Experience implementing human-in-the-loop workflows.10. Data and Integration
- Experience integrating AI agents with enterprise systems, APIs, databases, and SaaS platforms.
- Working knowledge of SQL/NoSQL databases and event-driven systems.11. Cloud Platforms
- Strong experience with Azure, AWS, or GCP.
- Knowledge of managed AI services, identity and access management, secrets management, and cost optimization.
- Azure experience is preferred.12. Good to Have
- Experience building enterprise copilots and autonomous workflows.
- Multi-agent collaboration and orchestration experience.
- Knowledge of reinforcement learning for agent optimization.