Description :

Mandatory Skills : AIML, LLM, Core AI, Azure, MCP, RAG, Docket/Kubernate

Location : Hyderabad [Madhapur]

Work mode : 2 Days WFO

Responsibilities :

- End-to-end design, development, and deployment of enterprise-grade AI solutions leveraging Azure AI, Google Vertex AI, or comparable cloud platforms.

- Architect and implement advanced AI systems, including agentic workflows, LLM integrations, MCP-based solutions, RAG pipelines, and scalable microservices.

- Oversee the development of Python-based applications, RESTful APIs, data processing pipelines, and complex system integrations.

- Define and uphold engineering best practices, including CI/CD automation, testing frameworks, model evaluation procedures, observability, and operational monitoring.

- Partner closely with product owners and business stakeholders to translate requirements into actionable technical designs, delivery plans, and execution roadmaps.

- Provide hands-on technical leadership, conducting code reviews, offering architectural guidance, and ensuring adherence to security, governance, and compliance standards.

- Communicate technical decisions, delivery risks, and mitigation strategies effectively to senior leadership and cross-functional teams.

Required Skills & Experience :

LLM & Core AI :

- Strong understanding of transformers (attention, tokens, context window) and LLM behavior.

- Hands-on with 2+ LLM providers (e.g., Azure OpenAI + Anthropic / open source like Llama/Qwen).

- Experience tuning decoding parameters and handling context window limits (truncation, sliding window, summarization).

Prompting & Context Engineering :

- Proven experience designing multi-layer prompts (system/policy, task, user, tools, retrieved context).

- Built context builders that select relevant history (recency + semantic) and inject tool + RAG outputs.

- Implemented context compression (conversation/memory summarization) and structured outputs (JSON/schema) with robust error handling.

Tools, MCP & External Integrations :

- Designed and implemented LLM tools/function schemas with validation, clear errors, and safe side-effects.

- Hands-on experience with MCP (Model Context Protocol): building MCP servers/tools for internal data and actions, including auth and multi-tenant isolation.

- Experience integrating REST/SQL/sandboxed execution tools and defining fallback/degradation strategies when tools fail.

Agentic Systems, Orchestration & A2A :

- Built multi-step agentic workflows: plan tool calls intermediate decisions final answer.

- Practical use of agent roles (Planner / Worker / Critic / Router / Supervisor).

- Hands-on with A2A (Agent-to-Agent) collaboration where specialist agents exchange structured state.

- Experience with at least one agentic/workflow framework (e.g., LangGraph, LangChain agents, Google ADK, Orkes Conductor, Temporal) and checkpointed, resumable flows (Postgres/Redis).

RAG & Knowledge Orchestration :

- Delivered end-to-end RAG systems: ingestion chunking embedding indexing retrieval synthesis.

- Implemented hybrid search (vector + keyword + filters) over enterprise sources (PDF, HTML, Confluence/SharePoint, SQL).

- Experience with query rewriting/expansion and grounded answers with citations, including debugging retrieval quality.

Reasoning, Evaluation & Guardrails :

- Implemented ReAct-style and tool-augmented reasoning patterns, including self-critique/second-pass flows.

- Defined task-level success metrics and built golden test flows from real logs to evaluate prompt/model/flow changes.

- Instrumented telemetry for tool errors, step counts, loops, latency, and cost (tokens, per feature/tenant).

- Implemented guardrails: prompt-injection defenses, per-tenant/per-role tool & data access, input/output filtering, PII-safe logging, and participated in red teaming/adversarial testing.

Model, Cost & Performance Engineering :

- Experience choosing and combining small router/classifier models with large reasoning models.

- Implemented caching (LLM outputs, retrieval results) and optimized latency (parallelization, step count, time budgets).

- Built or contributed to cost/usage monitoring for LLM and agent workflows.

Supporting Software Engineering :

- Expert-level proficiency in Python, RESTful API development, microservices architecture, and containerized deployments (Kubernetes, Docker).

- Experience with API frameworks such as FastAPI, FastMCP, Flask, Django, and tools like Swagger/OpenAPI.

- Hands-on background in data engineering, including data transformation, SQL/NoSQL databases, and event-driven architectures.

- Deep understanding of DevOps and MLOps practices, including CI/CD pipelines, infrastructure-as-code, observability platforms, model/workflow monitoring, security, and automated testing.

- Proven ability to collaborate with cross-functional teams, manage project timelines, and drive technical alignment in complex engineering environments.

- Exceptional communication and presentation skills with the ability to convey complex AI concepts to both technical and non-technical audiences.