Senior Python Engineer
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.
