Machine Learning Engineer position at RATP focuses on developing and optimizing Generative AI solutions. Engage with data science and machine learning within the company’s Data Factory.
About the role
- Staff Machine Learning Engineer to raise AI system design standards for B2B SaaS platform. Leading complex initiatives while building and operating production-grade AI/ML systems.
Responsibilities
- Lead the design and development of AI/ML systems starting from problem framing, decision boundaries, and operational constraints.
- Drive system and architecture decisions for AI and ML platforms, ensuring scalability, performance, and operational excellence.
- Design and evolve context engineering strategies for LLM-based systems under strict context window and latency constraints.
- Architect agent-based systems, including agent roles, interaction models, coordination strategies, and failure handling.
- Build and operate production AI systems with ownership of reliability, latency, cost, and correctness.
- Define evaluation frameworks for AI and agent behavior beyond offline model metrics.
- Scale AI-assisted development and reusable AI workflows to improve engineering velocity and system quality.
- Review and approve ML designs, guiding tradeoffs across model quality, system complexity, and business impact.
- Mentor senior engineers and data scientists, raising standards in AI system design, ML engineering, and production readiness.
- Partner with product and engineering leaders to translate ambiguous business problems into well-scoped, high-impact AI/ML solutions.
- Contribute to multi-quarter technical strategy, aligning AI and ML investments with company objectives.
Requirements
- 12+ years of experience across AI, ML, and software engineering roles.
- Strong coding skills in Python, Scala, or Java, with a track record of production-quality systems.
- Hands-on experience in building, evaluating, and operating LLM-powered systems in production.
- Strong understanding of context engineering, including information prioritization, relevance, and degradation under constraints.
- Experience designing AI workflows or agentic systems, including task decomposition, orchestration, and failure handling.
- Deep hands-on expertise in ML system design, NLP, feature engineering, and operational ML.
- Experience deploying AI/ML systems on at least one major cloud platform.
- Hands-on experience with Big Data or streaming systems (e.g., Spark, Kafka, Airflow).
- Proven ability to lead through influence, mentor engineers, and drive cross-team initiatives.
- Strong business and product mindset, with the ability to map AI/ML solutions to measurable outcomes.
Benefits
- Group Medical
- Personal Accident
- Term Life Insurance
- Preventive healthcare covers dental, vision, and OPD needs
- Strong mental health support
- Fitness benefit
- Car lease policy
- Gratuity for long-term financial well-being
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
No Education Requirement
Tech skills
Location requirements
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