Senior AI/ML Engineer

What You'll Do

• Engage with clients in the US to understand business objectives, translate them into AI/ML solution roadmaps, and communicate progress and results clearly to both technical and non-technical audiences.

• Lead the design, development, and optimisation of AI/ML models—including LLMs, RAG pipelines, embeddings, and predictive models—for complex, real-world client problems.

• Build end-to-end ML workflows on platforms like Databricks, from data ingestion and feature engineering to training, evaluation, deployment, and monitoring.

• Design and implement generative AI and agentic workflows that integrate LLMs with enterprise data sources, APIs, and existing business processes.

• Collaborate with data engineers, BI developers, and client stakeholders to prepare large, high-quality datasets and integrate models into production applications and data products.

• Establish and promote AI/ML best practices around experimentation, reproducibility, versioning, performance tracking, and model governance across projects.

• Mentor and guide junior team members, review code and solution designs, and provide technical leadership during architecture and strategy discussions.

What You Bring

• 4–7 years of industry experience in applied machine learning or data science, or 3–5 years combined with a recent Ph.D. in Computer Science, AI, Machine Learning, Statistics, or a related field.

• Strong proficiency in Python and core ML/AI libraries and frameworks (e.g., PyTorch, TensorFlow, scikit-learn, Hugging Face Transformers).

• Hands-on experience building and deploying LLM-based solutions, including embeddings, RAG architectures, and generative or conversational AI applications.

• Proven track record of developing predictive models for classification, regression, or forecasting, with solid understanding of algorithms, statistical modelling, and optimisation techniques.

• Significant experience working on Databricks or similar cloud-based data and ML platforms, including notebooks, ML runtimes, and scalable data processing.

• Exposure to MLOps practices and tools (e.g., MLflow, model registries, CI/CD, containerization) and deploying models to production in AWS, GCP, or Azure environments.

• Excellent analytical and problem-solving skills, with the ability to work independently, collaborate in distributed teams, and communicate complex concepts clearly to diverse stakeholders.

Nice to Have

• Master’s or Ph.D. in a quantitative discipline (Computer Science, Machine Learning, Statistics, Mathematics, or related field).

• Experience with building agentic AI workflows, tools, or orchestration frameworks for complex multi-step tasks.

• Familiarity with broader programming ecosystems such as R or C++ for performance-critical or legacy integration scenarios.

• Background in data-intensive consulting, analytics services, or working directly with enterprise clients across multiple domains.

• Experience implementing responsible AI practices, including model explainability, bias detection/mitigation, and compliance-aware data handling.

• Hands-on experience with feature stores, vector databases, and real-time inference setups.

  • Contributions to open-source ML/AI projects, publications, or technical blogging/speaking are a plus.