AI/ML Engineer

• Core Programming \& Systems Skills
• Python (expert level) for ML, orchestration, and agent logic
• Strong understanding of async programming, concurrency, and task scheduling
• Foundations of Agentic AI
• Design and implementation of autonomous AI agents capable of:

o Multi\-step reasoning and planning

o Goal decomposition and task orchestration

o Dynamic decision\-making under uncertainty

* Experience with agent architectures

o ReAct, Plan\-and\-Execute, Reflexive agents

o Hierarchical / multi\-agent systems

o Tool\-augmented and function\-calling agents

• Understanding of stateful vs stateless agents and memory management
• Large Language Models (LLMs)
• Hands\-on experience with LLMs (OpenAI, Azure OpenAI, Anthropic, open\-source models)
• Prompt\-engineering techniques for:

o Reasoning (Chain\-of\-Thought, Self\-Reflection)

o Planning and critique loops

o Instruction following and tool use

* Experience with

o Few\-shot and zero\-shot prompting

o Model selection trade\-offs (latency, cost, context length)

• Knowledge of fine\-tuning / adapters (LoRA) is a plus
• Agent Frameworks \& Tooling
• Practical experience with agent frameworks, such as:

o LangGraph / LangChain (agents, tools, memory)

o Semantic Kernel

o AutoGen, CrewAI, or similar

• Ability to build custom agent orchestration layers beyond frameworks
• Tool abstraction and execution safety (timeouts, retries, sandboxing)

Classification: Internal

• Memory, Context \& Knowledge Augmentation
• Design of agent memory systems:

o Short\-term (conversation/state memory)

o Long\-term (episodic, semantic memory)

* Retrieval\-Augmented Generation (RAG)

o Vector databases (FAISS, Pinecone, Azure AI Search, etc.)

o Embedding selection and chunking strategies

• Techniques for context management and compression
• Knowledge graph–augmented or hybrid memory (plus)
• Planning, Reasoning \& Control
• Experience implementing:

o Task planners (step planning, re\-planning)

o Constraint\-based execution

o Feedback and self\-correction loops

* Understanding of

o Tool reliability scoring

o Guardrails and action validation

o Failure detection and graceful recovery

• MLOps \& AgentOps
• Deployment of agents into production environments
• Observability for agents:

o Tracing agent decisions and tool calls

o Logging prompts, responses, and errors

• Model and prompt versioning
• CI/CD for agent systems
• Experience with Docker, Kubernetes, serverless deployments (Azure/AWS)
• Evaluation \& Testing of Agentic Systems
• Designing evaluation frameworks for agents:

o Task success rate

o Cost, latency, and reliability

o Safety and hallucination detection

• Offline test harnesses and simulation environments

Classification: Internal

• A/B testing of prompts, tools, and agent strategies
• Security, Safety \& Responsible AI
• Secure tool execution and privilege control
• Prompt\-injection and jailbreak risk mitigation
• Data privacy and isolation in agent memory
• Responsible AI practices:

o Bias awareness

o Explainability of agent decisions

o Human\-in\-the\-loop escalation patterns

10\. Data \& Integration Skills

* Integration with

o Enterprise systems (CRM, ERP, databases)

o Web services, internal APIs, and SaaS tools

* Working knowledge of

o SQL / NoSQL databases

o Event\-driven systems and message queues (plus)

11\. Cloud \& Platform Expertise

* Strong experience with at least one cloud platform

o Azure (preferred for enterprise agentic AI), AWS, or GCP

• Managed AI services, identity \& access, secrets management
• Cost optimization for LLM\-driven systems

12\. Bonus / Advanced Skills (Nice to Have)

• Multi\-agent collaboration and negotiation
• Human\-AI collaboration patterns (copilots, supervisors)
• Reinforcement learning for agent policy optimization
• Experience building enterprise copilots or autonomous workflows

Pay: ₹406,293\.45 \- ₹2,072,258\.15 per year

Work Location: Remote