AL/ML Engineer – 6-11 Years of Experience

Please categorize into 6-8 & 8-11

- Lead the implementation and composing of tool-using agents that interact with APIs, databases, and knowledge systems.
- Leading a team of AI/ML Engineers to implement multi-agent systems • Building persistent agent memory systems (short-, long-, and episodic memory).
- Implementing fault-tolerant orchestration for multi-agent pipelines.
- Building and scaling cloud-native systems (on AWS, Azure, or GCP).
- Simulation and testing of multi-agent interactions for scalability, safety, and emergent behaviors.
- Building guardrail systems using tools like Guardrails AI, NeMo Guardrails, or custom validators.
- Embedding compliance and observability hooks in every agent interaction.

Skills

- Core AI/ML Expertise o Deep understanding of transformer architectures, attention mechanisms, and LLM training pipelines.
- Agentic System Design o Understanding of agent architectures (e.g., ReAct, Reflexion, Voyager, AutoGPT, CrewAI, AutoGen). o Familiarity with agent orchestration frameworks (LangChain / LangGraph, Semantic Kernel, LlamaIndex, Swarm, etc.).



o Deep understanding of multi-agent communication protocols (e.g., MCP and A2A) — message passing, coordination, and negotiation strategies. o Designing hierarchical agents: planner, executor, verifier, critic, and memory manager roles. o Ability to balance autonomy vs. control, implementing "human-in-the-loop" governance mechanisms.
- AI / ML Engineering incl MLOps / LLMOps o Hands-on experience with Version control, CI/CD, and containerization (GitHub Actions, Docker, Kubernetes). o CI/CD for ML: model registry, versioning, and promotion (MLflow, Weights & Biases). o LLMOps: prompt evaluation, feedback loops, token optimization, cost monitoring. o Understanding of Continuous deployment of multi-agent pipelines via Argo CD, GitOps, or Terraform. o Observability for AI: telemetry on performance, latency, and behavioral drift.
- Knowledge, Context & Data Integration o Integration of vector databases for memory and retrieval. o Designing retrieval-augmented generation (RAG) pipelines with agile context injection

📌 AL/ML Engineer &ndash (Uttar Pradesh)
🏢 Sparix Global
📍 Uttar Pradesh