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 AIStrong 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 EngineeringProven 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 IntegrationsDesigned 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 & A2ABuilt multi-step agentic workflows: plan â tool calls â intermediate decisions â 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 OrchestrationDelivered end-to-end RAG systems: ingestion â chunking â embedding â indexing â retrieval â 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 & GuardrailsImplemented 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 EngineeringExperience 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 EngineeringExpert-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.
