Python Tech Lead

Job description

About Sutherland Artificial Intelligence. Automation.Cloud engineering. Advanced analytics.For business leaders, these are key factors of success. For us, they’re our core expertise. We work with iconic brands worldwide. We bring them a unique value proposition through market-leading technology and business process excellence. We’ve created over 200 unique inventions under several patents across AI and other critical technologies. Leveraging our advanced products and platforms, we drive digital transformation, optimize critical business operations, reinvent experiences, and pioneer new solutions, all provided through a seamless “as a service” model. For each company, we provide new keys for their businesses, the people they work with, and the customers they serve. We tailor proven and rapid formulas, to fit their unique DNA.We bring together human expertise and artificial intelligence to develop digital chemistry. This unlocks new possibilities, transformative outcomes and enduring relationships. Sutherland Unlocking digital performance. Delivering measurable results. We are seeking an experienced Python Tech Lead / Associate Manager to lead our AI development initiatives, with deep expertise in building production-grade agentic AI systems using LangChain, LangGraph, and related frameworks. This role combines technical leadership with hands-on development, requiring someone who can architect complex AI solutions while mentoring a team of developers. Key Responsibilities Technical Leadership • Architect and design scalable agentic AI systems using LangChain and LangGraph frameworks • Lead the development of autonomous AI agents with multi-step reasoning and decision-making capabilities • Establish best practices for prompt engineering, agent orchestration, and AI system reliability • Drive technical decisions on framework selection, tool integration, and system architecture • Conduct code reviews and ensure high-quality, maintainable codebases Hands-on Development • Build complex agent workflows using LangGraph's state machines and conditional logic • Implement multi-agent systems with tool calling, memory management, and retrieval mechanisms • Develop custom chains, agents, and tools within the LangChain ecosystem • Optimize LLM performance through prompt tuning, caching strategies, and efficient API usage • Integrate vector databases, embeddings, and retrieval-augmented generation (RAG) pipelines Team Management • Mentor and guide a team of 3-7 developers in AI/ML development practices • Facilitate knowledge sharing sessions on agentic AI patterns and emerging technologies • Coordinate sprint planning, task allocation, and delivery timelines • Foster a culture of innovation and continuous learning within the team Core Python & AI Frameworks • 6+ years of Python development experience with strong expertise in async programming, type hints, and modern Python patterns • 2+ years hands-on experience with LangChain and LangGraph building production applications • Deep understanding of agentic AI architectures including ReAct, Plan-and-Execute, and Reflection patterns • Experience with LangChain Expression Language (LCEL) for chain composition • Experience with developing and maintaining FastAPI projects with React Framework. Agentic AI Specific Modules • LangGraph: StateGraph, MessageGraph, conditional edges, human-in-the-loop patterns, checkpointing and persistence • LangChain Core: Agents (OpenAI Functions, Structured Chat, ReAct), Tools, Toolkits, Memory systems (ConversationBufferMemory, ConversationSummaryMemory, VectorStoreMemory) • LangChain Callbacks: Custom callback handlers, tracing, logging, and observability • Agent Executors: AgentExecutor configuration, error handling, and timeout management • Multi-Agent Systems: Agent coordination, communication protocols, task delegation LLM & Model Integration • Experience with OpenAI GPT-4/GPT-3.5, Anthropic Claude, or open-source models (Llama, Mistral) • Understanding of model selection, cost optimization, and fallback strategies • Prompt engineering expertise including few-shot learning, chain-of-thought prompting • Experience with function calling and structured outputs from LLMs Vector Databases & Retrieval • Hands-on experience with vector databases: Pinecone, Weaviate, Chroma, or FAISS • Implementation of RAG pipelines with document chunking, embedding generation, and similarity search • Knowledge of semantic search, hybrid search, and re-ranking techniques • Experience with embedding models (OpenAI embeddings, Sentence Transformers, Cohere) Additional AI/ML Frameworks • LlamaIndex for advanced data indexing and querying • Haystack or Semantic Kernel as alternative orchestration frameworks • Hugging Face Transformers for custom model integration • MLflow or Weights & Biases for experiment tracking Data & Integration • API integration experience (REST, GraphQL, webhooks), Frameworks : FastApi • Document processing: PDF parsing, OCR, web scraping (Beautiful Soup, Scrapy) • Database expertise: PostgreSQL, MongoDB, Redis for caching and session management • Message queues: RabbitMQ, Kafka for asynchronous agent communication DevOps & Production • Docker and Kubernetes for containerization and orchestration <