Senior AIML Engineer

WILLWARE TECHNOLOGIES PRIVATE LIMITED

Job Location:

Hyderabad - India

Monthly Salary: Not Disclosed

Posted on: 2 hours ago

Vacancies: 1 Vacancy

Job Summary

company : Willware technology
Role: AI/ML Engineer
work mode: Hybrid

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.

Required Skills:

CoreAIDockerKubernetesragmcp

company : Willware technologyRole: AI/ML Engineerwork mode: Hybrid 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 workf...