Generative AI Engineer at Sia Partners - United Kingdom

Mumbai, maharashtra, India -

Full Time

Start Date

Immediate

Expiry Date

04 May, 26

Salary

0.0

Posted On

03 Feb, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Generative AI, NLP, Machine Learning, Python, Cloud Services, Prompt Engineering, MLOps, AI Solutions, Data Science, Vector Databases, Multimodal AI, DevOps, APIs, Benchmarking, Fine-tuning, Collaboration

Industry

Business Consulting and Services

Description

Company Description Sia Partners is a next-generation global management consulting firm, founded in 1999 and headquartered in Paris, France. The firm is recognised for its innovative approach, combining strategy and management consulting with data science and creativity. Sia Partners serves a diverse range of sectors, including energy, banking, healthcare, and technology, providing services to over 1,000 clients worldwide, including many Fortune 500 companies. With a strong emphasis on delivering tangible results and superior value, Sia Partners is committed to helping clients navigate the digital revolution and achieve transformation. The firm operates with a global presence, employing over 3,500 consultants across 48 locations in 20 countries Our Mumbai office was launched in 2024, marking an exciting phase for us. We’re building the team here with people who are excited about the opportunity to shape something from the ground up — having the best of 2 worlds- the agility and entrepreneurial energy of a startup with the backing and reach of a global brand. Job Description We are seeking a skilled Generative AI Engineer to join our team where you will harness model capabilities to implement cutting-edge algorithms and solutions across myriad industries. You will serve as a pivotal link between Data Scientists, ML and Platform Engineers to unleash the potential of Generative AI technology by implementing business-centric solutions. You will help customers find the appropriate level of refinement among semantic search, RAG, agents, and ultimately fine-tuning to reach their value delivery threshold in the most cost-effective way. Beyond crafting prompts, you will be responsible for designing and building robust and scalable products starting with benchmarks of candidate FMs through targeted requests, rapidly iterating prototypes, and validating product ideas. Your expertise in orchestrating the entire AI workflow will ensure the seamless integration of advanced models' capabilities into applications, optimizing performance, security, compliance, scalability, and efficiency. You will competently navigate between prompts, chains, and agents while mastering the underlying infrastructure challenges. We invest in your success through comprehensive training, combining internal programs with resources from our technology partners. Join us if you are passionate about pushing the boundaries of AI technology and making a significant impact in enabling our customers to create GenAI-powered applications with confidence and a fast time to market. Key Responsibilities You are part of a cross-functional consulting team that drives the adoption of Generative AI in every imaginable sector, working step-by-step with customers to understand business requirements to design then build bespoke GenAI solutions. Build applications powered by LLMs (OpenAI, Claude, Mistral, etc.) using LangChain, LlamaIndex, and related GenAI frameworks. Implement RAG pipelines with vector DBs (Pinecone, FAISS, pgvector, ChromaDB) for grounding LLM responses with internal knowledge Develop multimodal AI solutions (text, audio, image) and build autonomous agents where relevant. Drive MLOps excellence: CI/CD (ML pipelines), drift detection, canary releases, retraining schedules. Design robust and reusable prompt templates using CoT, ReAct, Graph-of-Thought, and Agent flows. Continuously improve model reliability, relevance, and UX by tuning prompt flows Deploy GenAI models on AWS/GCP/Azure using services like SageMaker, Bedrock, Vertex AI Ensure performance observability, security guardrails, and compliance (GDPR, Responsible AI) Work with DevOps teams to integrate GenAI solutions into microservices and APIs (FastAPI/Flask) Benchmark open-source and commercial LLMs for use-case fit and cost-performance tradeoffs Evaluate fine-tuning strategies (PEFT, LoRA, RLHF) where applicable for proprietary use cases Support solution architects and cross-functional teams in delivering PoCs and enterprise-grade rollouts Document frameworks, best practices, risks, and learnings for future scaling Qualifications Bachelor’s/master's degree in computer science, AI, or a related field. 5+ years of experience in NLP/ML/AI with at least 3 year hands-on in GenAI. Strong coding skills in Python with frameworks like PyTorch, Hugging Face, LangChain, and LlamaIndex. Proven experience with cloud-based AI services (AWS/GCP/Azure) and APIs (OpenAI, Anthropic, Hugging Face). Experience with vector databases: Qdrant, pgvector, Pinecone, FAISS, Milvus, or Weaviate. Familiarity with prompt engineering, transformer architectures, and embedding techniques. Excellent communication skills, with the ability to convey complex technical concepts to both highly technical and also non-technical stakeholders. Sharp problem-solving skills. Ability to collaborate with diverse teams. Additional Information What We Offer Opportunity to lead cutting-edge AI projects in a global consulting environment. Leadership development programs and training sessions at our global centers. A dynamic and collaborative team environment with diverse projects. Position based in Mumbai (onsite) Sia is an equal opportunity employer. All aspects of employment, including hiring, promotion, remuneration, or discipline, are based solely on performance, competence, conduct, or business needs.

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Responsibilities

You will be part of a cross-functional consulting team that drives the adoption of Generative AI across various sectors, working closely with customers to design and build bespoke solutions. Your role includes building applications powered by large language models and implementing RAG pipelines for grounding responses with internal knowledge.