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What is the opportunity? We’re looking a Lead AI/ML Software Engineer to drive innovation at the intersection of AI / Machine Learning and financial services. You’ll be responsible for owning and delivering projects…
Responsibilities Required to translate technical systems specifications into working, tested This includes: developing detailed programming specifications writing and/or generating code compiling data-driven programs,…
Data Science Engineer developing, validating, and deploying ML, Deep Learning, and Generative AI models into production, while building data pipelines and supporting model monitoring using Python, SQL, and standard data science libraries.
Lead a team to design and deploy AI-powered cybersecurity applications using Python, ML frameworks, and Kubernetes, integrating security tools and REST APIs.
This Manager role at Deloitte involves leading AI engineering workstreams to build and scale production-ready AI and Generative AI solutions for Civil Government clients. The position requires hands-on engineering expertise in Python and cloud platforms, combined with stakeholder management and delivery leadership in secure, regulated environments.
Builds and maintains data pipelines, warehouses, and collection systems to enable AI/data science projects for clients, focusing on security, scalability, and cloud infrastructure (AWS/GCP/Azure).
The Data Scientist / Machine Learning Engineer will design, train, and deploy AI/ML models into production environments using Python, deep learning frameworks, and cloud platforms like Azure or Google Cloud. The role involves working on NLP, clustering, and generative AI projects while ensuring compliance with responsible AI standards.
Leads end-to-end design and deployment of production-grade AI/ML and GenAI solutions, focusing on RAG platforms, agentic systems, and secure enterprise integrations using Python, AWS/Azure, and MLOps practices.
Build and deploy end-to-end GenAI/LLM applications (RAG, agentic workflows, evaluation pipelines) using Python, cloud platforms, Docker, and vector databases for a finance consulting firm in Pune.
Staff ML Engineer architecting and deploying production GenAI systems (LLM-powered features, RAG workflows, agentic apps) on a team building AI-powered identity/security infrastructure, primarily using Python.
4-month co-op on an AI & Enterprise Applications team, designing and developing software applications and AI/ML solutions using Python/C#/.NET/Java, cloud platforms (Azure), and ML frameworks like TensorFlow and PyTorch.
Build AI models for contact-center knowledge assist using Python and deep-learning frameworks like PyTorch or TensorFlow.
Design and deploy production-scale GenAI systems, including RAG pipelines, agent frameworks, and evaluation workflows, to power secure AI-powered identity features.
4-month co-op on the AI & Enterprise Applications team helping design, develop, and deploy software and AI/ML solutions using Python, C#/.NET, Java, TensorFlow/PyTorch, and Azure.
Builds GenAI and ML systems for actuarial finance, translating business problems into production-ready predictive/analytical solutions with explainability, governance, and operational controls.
Designs and deploys enterprise-scale Generative AI systems, focusing on LLMs, agentic workflows, RAG pipelines, and scalable microservices for AI-driven applications.
ML Engineering Intern on the Knowledge Assist team building RAG, generative knowledge assist, and enterprise search solutions for contact centers using Python and deep learning frameworks.
Onsite Software Developer building REST APIs and web applications with React, TypeScript, Node.js, and Next.js, plus AI/ML integrations (Agentic AI, RAG, LLMs) for a government/public sector client in Toronto or Peterborough.
Senior Full Stack Developer builds and maintains engineering tools in Python (FastAPI) to help MEMS developers design and produce precise timing devices.
Lead the design and delivery of an agentic digital assistant inside Microsoft Teams for a major financial services client, building LLM-driven features, RAG pipelines, and persona-driven reporting using Python, AWS, and GenAI/LLM technologies.
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