• Design, develop, and deploy advanced AI solutions focused on generative capabilities and conversational Al. • Build context-aware, autonomous AI agents using modern frameworks to solve complex business logic and automate workflows. • Collaborate with data engineers to preprocess and curate large datasets for training and testing models. • Experiment with state-of-the-art machine learning algorithms and fine-tune foundation models for production. • Build and deploy scalable, production-ready AI pipelines. • Analyze and optimize the performance of AI models to meet business requirements. • Stay updated with the latest advancements in GenAI, LLMs, and agentic systems, and propose innovative solutions to enhance products.

Role Description We are looking for a passionate and skilled AI engineer who is responsible for designing and delivering complex generative AI solutions. This role requires the ability to work autonomously on challenging technical problems, provide mentorship to junior engineers and champion engineering best practices. - Design, build and deploy GenAI applications using techniques such as RAG, Agents, Multi Agent Systems, etc., taking ideas from prototype to production. - Work with both AI/ML engineers and software engineers to deliver reliable, scalable systems. - Own key features or components, ensuring they are well-structured, efficient and easy to maintain. - Make technical decisions and contribute to system design with attention to performance, cost and reliability. - Collaborate with product managers, designers and data scientists to turn business needs into practical solutions. - Share knowledge and mentor junior team members, helping them grow their technical skills. - Review code and provide clear, constructive feedback to peers. - Understand deployment options and trade-offs across different cloud providers such as AWS, Azure, Google Cloud, etc. - Understand optimization techniques to improve accuracy, performance and cost for GenAI applications. Qualifications - Strong software engineering fundamentals, including Python, CI/CD, testing, version control and clean code practices. - Solid grasp of software design principles and ability to design and implement AI-powered components or workflows. - Experience with at least one GenAI framework (e.g., LangChain, LlamaIndex, Semantic Kernel) and one agentic framework (e.g., PydanticAI, LangGraph, AutoGen2). - Hands-on experience building and optimizing Retrieval-Augmented Generation (RAG) pipelines with vector databases (e.g., FAISS, Pinecone, Weaviate). - Familiarity with deploying AI solutions on major cloud platforms (AWS, Azure or GCP), with basic use of containers (Docker) and CI/CD pipelines. - Experience using LLMOps and observability tools (e.g., Langfuse, PromptLayer, OpenTelemetry) in production. - Exposure to fine-tuning and use of ML/NLP frameworks such as PyTorch or Hugging Face Transformers. Requirements - Ability to independently solve complex, ambiguous technical problems. - Willingness and ability to guide and mentor junior engineers, sharing knowledge and best practices. - Strong collaborative skills, working effectively with diverse, cross-functional teams. - Thrives in a dynamic and fast-paced environment, demonstrating resilience in the face of challenges. Benefits - There is no one-size-fits-all career path at Thoughtworks; your career development is entirely up to you. - Support through interactive tools, numerous development programs and teammates who want to help you grow. - Value in helping each other be our best, extending to empowering employees in their career journeys. Company Description Thoughtworks is a dynamic and inclusive community of bright and supportive colleagues who are revolutionizing tech. As a leading technology consultancy, we’re pushing boundaries through our purposeful and impactful work. For 30+ years, we’ve delivered extraordinary impact together with our clients by helping them solve complex business problems with technology as the differentiator. Bring your brilliant expertise and commitment for continuous learning to Thoughtworks. Together, let’s be extraordinary.

• Consult clients during presales to assess AI readiness, constraints, and success criteria, translating visions into actionable requirements • Prepare and present presales deliverables including architecture diagrams, assumptions, risks, estimates, scalability considerations, and implementation roadmaps • Define AI use cases and agentic scenarios based on client needs • Select and justify LLMs per use case based on requirements, cost, latency, safety, capability • Architect multi-agent frameworks with orchestrator agents and MCP-style protocols • Design and implement single-agent/multi-agent AI systems with defined roles, tool access, memory, safety boundaries • Build orchestration logic (routing, delegation, retries, fallback strategies, consensus, human-in-the-loop flows) • Develop RAG pipelines (data ingestion, chunking, embeddings, vector databases, hybrid retrieval, relevance optimization) • Implement learning & feedback loops for continuous agent improvement • Design custom or adapted models (prompt-tuned agents, LoRA fine-tuning, domain-inherited models)

• Design and optimize RAG pipelines over domain-specific content • Improve retrieval quality, ranking, and grounding to reduce hallucinations • Build evaluation frameworks to measure accuracy and consistency • Work with fragmented enterprise data and make it usable • Build AI-powered features integrated into STARLIMS workflows • Design for reliability (latency, scale, model variability) • Implement guardrails, fallbacks, and observability • Manage prompt evolution, model drift, and regressions • Develop systems that can reason, call tools, and execute multi-step tasks • Integrate with internal APIs, developer tooling, and external systems • Build and operate backend services on AWS (Lambda, API Gateway, DynamoDB, etc.) • Own system architecture and key technical decisions • Contribute to infrastructure-as-code and deployment pipelines