AI/ ML Engineer (Python)

Every day, somewhere in the world, important decisions are made. Whether it is a private equity company deciding to invest millions into a business or a large corporation implementing a new strategic direction, these decisions impact employees, customers, and other stakeholders. 

Consulting and private equity firms come to proSapient when they need to discover knowledge to help them make great decisions and succeed in their goals. It is our mission to support them in their discovery of knowledge. 

We help our clients find industry experts who can provide their knowledge via interview or survey: we curate this knowledge in a market-leading software platform; and we help clients surface knowledge they already have through expansive knowledge management.  

We are seeking an AI/ML Engineer to contribute to our AI/ML initiatives with a strong emphasis on practical software engineering using Python. This is a hands-on role combining AI/LLM-based data extraction, data analysis through various experiments, advanced recommendation engines, and backend development. You’ll work across the stack—from exploring business problems and adapting AI models to building production-grade systems that integrate into our platform. 

This position is ideal for someone who is both analytically strong and technically capable, excited to apply AI/ML techniques to real-world challenges like expert recommendations, content extraction, and workflow automation. 

Why Join Us? 

• Help shape meaningful AI-powered products with global reach. 

• Join a cross-functional, fast-moving team of engineers, data scientists, and product managers. 

• Work on modern ML systems in a collaborative, supportive environment. 

• Competitive compensation and opportunities for career growth and technical leadership. 

Key duties in this role will include: 

• Translate business problems into structured data science solutions with measurable outcomes. 

• Design and build production-grade solutions using LLM providers, Python, and databases. 

• Conduct and analyse experiments to evaluate the quality of data processing pipelines. 

• Work with real-time data streams and search infrastructure using tools like Kafka and Elasticsearch. 

• Present findings and recommendations to both technical and non-technical stakeholders. 

•  Strong Python programming skills, with practical experience writing clean, production-ready code. 

• Solid foundation with LLMs, including prompt engineering, fine-tuning, or retrieval-augmented generation (RAG). 

• Experience using structured prompt-engineering and evaluation tools (e.g., LangSmith, PromptLayer). 

• Familiarity with data science libraries and pipelines (e.g., pandas, spaCy). 

• Experience with Python web frameworks (e.g., FastAPI, Django) and RESTful APIs. 

• Proficiency in working with PostgreSQL or other relational databases. 

• Strong hands-on experience with Elasticsearch, including performance optimizations and query tuning. 

Bonus Skills 

• Experience with message brokers like Kafka or RabbitMQ. 

• Familiarity with Docker, Kubernetes, and cloud environments (preferably AWS). 

• Exposure to observability tools (e.g., Prometheus, Datadog) and background job processing frameworks. 

• Tenure Gifts - Vouchers, extra holiday and sabbaticals for each year of employment. 

• Health insurance through Vitality  

• Enjoy the flexibility of working remotely for up to 20 days each year, allowing you to tailor your work environment to your needs and embrace a change of scenery. 
• Enhanced Maternity & Paternity pay. 
• Corporate Events - From quarterly gatherings to our annual winter & Summer parties, we love to celebrate, collaborate and have fun together!  

We are committed to building an inclusive workplace – did you know that marginalized groups are less likely to apply to jobs unless they meet every requirement listed? If you are interested in the above role, but don’t necessarily tick every box, we encourage you to apply anyway – this role could still be a great match! Take a look at our diversity statement here.