Core Engineering ETO Machine Learning Engineer AssociateVP Bengaluru

Bengaluru - India

Monthly Salary: Not Disclosed

Posted on: Yesterday

Vacancies: 1 Vacancy

Job Summary

Description

What We Do

At Goldman Sachs our Engineers dont just make things we make things possible. Change the world by connecting people and capital with ideas. Solve the most challenging and pressing engineering problems for our clients. Join our engineering teams that build massively scalable software and systems architect low latency infrastructure solutions proactively guard against cyber threats and leverage machine learning alongside financial engineering to continuously turn data into action. Create new businesses transform finance and explore a world of opportunity at the speed of markets.

Engineering which is comprised of our Technology Division and global strategists groups is at the critical center of our business and our dynamic environment requires innovative strategic thinking and immediate real solutions. Want to push the limit of digital possibilities Start here.

Who We Look For

Goldman Sachs Engineers are innovators and problem-solvers building solutions in risk management big data mobile and more. We look for creative collaborators who evolve adapt to change and thrive in a fast-paced global environment.

BUSINESS UNIT OVERVIEW

Enterprise Technology Operations (ETO) is a Business Unit within Core Engineering focused on running scalable production management services with a mandate of operational excellence and operational risk reduction achieved through large scale automation best-in-class engineering and application of data science and machine learning. The Production Runtime Experience (PRX) team in ETO applies software engineering and machine learning to production management services processes and activities to streamline monitoring alerting automation and workflows.

TEAM OVERVIEW

The Machine Learning and Artificial Intelligence team in PRX applies advanced ML and GenAI to reduce the risk and cost of operating the firms large-scale compute infrastructure and extensive application estate. Building on strengths in statistical modelling anomaly detection predictive modelling and time-series forecasting we leverage foundational LLM Models to orchestrate multi-agent systems for automated production management services. By unifying classical ML with agentic AI we deliver reliable explainable and cost-efficient operations at scale.

ROLE AND RESPONSIBILITIES

In this role you will be responsible for launching and implementing GenAI agentic solutions aimed at reducing the risk and cost of managing large-scale production environments with varying complexities. You will address various production runtime challenges by developing agentic AI solutions that can diagnose reason and take actions in production environments to improve productivity and address issues related to production support.

What youll do:

Build agentic AI systems: Design and implement tool-calling agents that combine retrieval structured reasoning and secure action execution (function calling change orchestration policy enforcement) following MCP protocol. Engineer robust guardrails for safety compliance and least-privilege access.

Productionize LLMs: Build evaluation framework for open-source and foundational LLMs; implement retrieval pipelines prompt synthesis response validation and self-correction loops tailored to production operations.

Integrate with runtime ecosystems: Connect agents to observability incident management and deployment systems to enable automated diagnostics runbook execution remediation and post-incident summarization with full traceability.

Collaborate directly with users: Partner with production engineers and application teams to translate production pain points into agentic AI roadmaps; define objective functions linked to reliability risk reduction and cost; and deliver auditable business-aligned outcomes.

Safety reliability and governance: Build validator models adversarial prompts and policy checks into the stack; enforce deterministic fallbacks circuit breakers and rollback strategies; instrument continuous evaluations for usefulness correctness and risk.

Scale and performance: Optimize cost and latency via prompt engineering context management caching model routing and distillation; leverage batching streaming and parallel tool-calls to meet stringent SLOs under real-world load.

Build a RAG pipeline: Curate domain-knowledge; build data-quality validation framework; establish feedback loops and milestone framework maintain knowledge freshness.

Raise the bar: Drive design reviews experiment rigor and high-quality engineering practices; mentor peers on agent architectures evaluation methodologies and safe deployment patterns. .

QUALIFICATIONS

A Bachelors degree (Masters/ PhD preferred) in a computational field (Computer Science Applied Mathematics Engineering or in a related quantitative discipline) with 5 years of experience as an applied data scientist / machine learning engineer.

ESSENTIAL SKILLS

5 years of software development in one or more languages (Python C/C Go Java); strong hands-on experience building and maintaining large-scale Python applications preferred.

3 years designing architecting testing and launching production ML systems including model deployment/serving evaluation and monitoring data processing pipelines and model fine-tuning workflows.

Practical experience with Large Language Models (LLMs): API integration prompt engineering finetuning/adaptation and building applications using RAG and tool-using agents (vector retrieval function calling secure tool execution).

Understanding of different LLMs both commercial and open source and their capabilities (e.g. OpenAI Gemini Llama Qwen Claude).

Solid grasp of applied statistics core ML concepts algorithms and data structures to deliver efficient and reliable solutions.

Strong analytical problem-solving ownership and urgency; ability to communicate complex ideas simply and collaborate effectively across global teams with a focus on measurable business impact.

Preferred: Proficiency building and operating on cloud infrastructure (ideally AWS) including containerized services (ECS/EKS) serverless (Lambda) data services (S3 DynamoDB Redshift) orchestration (Step Functions) model serving (SageMaker) and infra-as-code (Terraform/CloudFormation).

ABOUT GOLDMAN SACHS

At Goldman Sachs we commit our people capital and ideas to help our clients shareholders and the communities we serve to grow. Founded in 1869 we are a leading global investment banking securities and investment management firm. Headquartered in New York we maintain offices around the world.

We believe who you are makes you better at what you do. Were committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally from our training and development opportunities and firmwide networks to benefits wellness and personal finance offerings and mindfulness programs. Learn more about our culture benefits and people at Were committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process. Learn more: The Goldman Sachs Group Inc. 2023. All rights reserved.

Goldman Sachs is an equal opportunity employer and does not discriminate on the basis of race color religion sex national origin age veterans status disability or any other characteristic protected by applicable law.

Required Experience:

Exec

Description What We Do At Goldman Sachs our Engineers dont just make things we make things possible. Change the world by connecting people and capital with ideas. Solve the most challenging and pressing engineering problems for our clients. Join our engineering teams that build massively scalable s...