AI/ML Engineer Technical Skill Set (Agentic AI Focus)

AI/ML Engineer Technical Skill Set (Agentic AI Focus) 1. Core Programming & Systems Skills Python (expert level) for ML, orchestration, and agent logicStrong understanding of async programming, concurrency, and task scheduling2. Foundations of Agentic AI Design and implementation of autonomous AI agents capable of:Multistep reasoning and planningGoal decomposition and task orchestrationDynamic decisionmaking under uncertaintyExperience with agent architectures:ReAct, PlanandExecute, Reflexive agentsHierarchical / multiagent systemsToolaugmented and functioncalling agentsUnderstanding of stateful vs stateless agents and memory management3. Large Language Models (LLMs) Handson experience with LLMs (OpenAI, Azure OpenAI, Anthropic, opensource models)Promptengineering techniques for:Reasoning (ChainofThought, SelfReflection)Planning and critique loopsInstruction following and tool useExperience with:Fewshot and zeroshot promptingModel selection tradeoffs (latency, cost, context length)Knowledge of finetuning / adapters (LoRA) is a plus4. Agent Frameworks & Tooling Practical experience with agent frameworks, such as:LangGraph / LangChain (agents, tools, memory)Semantic KernelAutoGen, CrewAI, or similarAbility to build custom agent orchestration layers beyond frameworksTool abstraction and execution safety (timeouts, retries, sandboxing)5. Memory, Context & Knowledge Augmentation Design of agent memory systems:Shortterm (conversation/state memory)Longterm (episodic, semantic memory)RetrievalAugmented Generation (RAG):Vector databases (FAISS, Pinecone, Azure AI Search, etc.)Embedding selection and chunking strategiesTechniques for context management and compressionKnowledge graphaugmented or hybrid memory (plus)6. Planning, Reasoning & Control Experience implementing:Task planners (step planning, replanning)Constraintbased executionFeedback and selfcorrection loopsUnderstanding of:Tool reliability scoringGuardrails and action validationFailure detection and graceful recovery7. MLOps & AgentOps Deployment of agents into production environmentsObservability for agents:Tracing agent decisions and tool callsLogging prompts, responses, and errorsModel and prompt versioningCI/CD for agent systemsExperience with Docker, Kubernetes, serverless deployments (Azure/AWS)8. Evaluation & Testing of Agentic Systems Designing evaluation frameworks for agents:Task success rateCost, latency, and reliabilitySafety and hallucination detectionOffline test harnesses and simulation environmentsA/B testing of prompts, tools, and agent strategies9. Security, Safety & Responsible AI Secure tool execution and privilege controlPromptinjection and jailbreak risk mitigationData privacy and isolation in agent memoryResponsible AI practices:Bias awarenessExplainability of agent decisionsHumanintheloop escalation patterns10. Data & Integration Skills Integration with:Enterprise systems (CRM, ERP, databases)Web services, internal APIs, and SaaS toolsWorking knowledge of:SQL / NoSQL databasesEventdriven systems and message queues (plus)11. Cloud & Platform Expertise Strong experience with at least one cloud platform:Azure (preferred for enterprise agentic AI), AWS, or GCPManaged AI services, identity & access, secrets managementCost optimization for LLMdriven systems12. Bonus / Advanced Skills (Nice to Have) Multiagent collaboration and negotiationHumanAI collaboration patterns (copilots, supervisors)Reinforcement learning for agent policy optimizationExperience building enterprise copilots or autonomous workflowsWork Location: Hybrid remote in Noida, Uttar Pradesh (Noida) .