Machine Learning Engineer - Associate/VP

Goldman Sachs typically conducts a 4‑stage interview process: an online aptitude/coding test, followed by one or two technical interviews focusing on data structures, algorithms and machine‑learning case studies, a managerial interview to assess problem‑solving approach and cultural fit, and finally an HR round covering motivations and compensation. The difficulty is high, so thorough preparation on system design, LLM concepts and coding speed is recommended.

Goldman Sachs fosters a high‑performance culture that rewards innovation, collaboration and continuous learning. Employees benefit from structured mentorship, global mobility options and a strong emphasis on work‑life balance through flexible hours, wellness programs and generous leave policies.

Bachelor’s (or Master’s/PhD) degree in Computer Science, Applied Mathematics, Engineering or related quantitative discipline; minimum 60% CGPA or equivalent; graduating batch 2024‑2027; no active backlogs at the time of joining; strong foundation in algorithms, data structures and statistics.

Round 1: Online Assessment → Round 2: Technical Interview(s) (coding, system design, ML case studies) → Round 3: Managerial/Leadership Interview → Round 4: HR Interview

Goldman Sachs is a premier global investment banking, securities and investment management firm founded in 1869. With its headquarters in New York and a presence in over 60 cities worldwide, the firm is known for its deep expertise, innovative culture and commitment to client service. In India, Goldman Sachs has built a strong technology hub that powers the firm’s trading, risk, and analytics platforms, offering fresh talent the chance to work on mission‑critical systems that impact markets worldwide. The company places a high value on diversit y, inclusion and continuous learning, providing employees with mentorship programs, internal mobility and a suite of wellness benefits. The Machine Learning Engineer – Associate/VP role sits within the Enterprise Technology Operations (ETO) Business Unit of Core Engineering. ETO’s Production Runtime Experience (PRX) team focuses on automating and optimizing large‑scale compute and application estates. The Machine Learning and AI sub‑team applies cutting‑edge Large Language Models (LLMs) and generative AI to create agentic solutions that can diagnose, reason and act on production incidents, thereby reducing operational risk and cost. In this role you will be responsible for designing, building and productionising GenAI‑driven agents that integrate with observability, incident‑management and deployment tools. You will work closely with production engineers to translate pain points into AI‑powered workflows, enforce safety guardrails, and continuously monitor model performance. The position offers a clear growth path from Associate to Vice‑President and beyond, with opportunities to lead cross‑functional AI initiatives, influence architecture decisions and mentor junior engineers. Key responsibilities include: 1. Design and implement tool‑calling agents that combine retrieval, structured reasoning and secure action execution. 2. Build evaluation frameworks for open‑source and commercial LLMs, including prompt synthesis, response validation and self‑correction loops. 3. Integrate agents with observability platforms, incident‑management systems and CI/CD pipelines for automated diagnostics and remediation. 4. Partner with product and engineering teams to define objective functions linked to reliability, risk reduction and cost savings. 5. Implement safety, governance and compliance mechanisms such as validator models, adversarial testing and deterministic fallbacks. 6. Optimize latency and cost through prompt engineering, context management, caching, model routing and distillation. 7. Develop and maintain a Retrieval‑Augmented Generation (RAG) pipeline with domain‑specific knowledge bases and quality‑control checks. 8. Lead design reviews, enforce engineering best practices and mentor peers on agentic AI architectures. 9. Drive experimentation rigor, performance benchmarking and continuous improvement of AI services. 10. Contribute to documentation, knowledge‑sharing sessions and community building within the AI team. Tech stack: Python, PyTorch/TensorFlow, LangChain, OpenAI/Gemini/Claude APIs, vector stores (FAISS, Pinecone), AWS (SageMaker, Lambda, ECS/EKS), Terraform, Docker, Kubernetes, Grafana/Prometheus for monitoring. Why join Goldman Sachs? You will work on high‑impact, real‑time financial systems, collaborate with world‑class engineers and data scientists, and gain exposure to the latest generative‑AI technologies. The firm’s strong brand, competitive compensation and clear promotion pathways make it an ideal place for ambitious engineers to accelerate their careers.