Machine Learning Engineer – Advanced
SUMMARY
The Machine Learning Engineer (Advanced) is a technical leader responsible for designing, building, and scaling production-grade AI systems that drive measurable business impact. This role goes beyond model development to owning end-to-end AI solutions—from problem definition through deployment, adoption, and continuous improvement.
Operating within the AI Producer model, this role partners with Product, Engineering, and Delivery teams to translate high-value opportunities into scalable AI capabilities, including agentic workflows and GenAI-powered systems.
KEY RESPONSIBILITIES
- Own end-to-end AI solution lifecycle from problem definition, experimentation, and model development to production deployment, adoption, and impact measurement
- Design and architect scalable ML/AI systems, including data pipelines, model training, evaluation, serving, monitoring, and retraining
- Build and deploy GenAI and agentic AI solutions, including LLM-based systems, RAG pipelines, and multi-agent workflows integrated into enterprise applications
- Drive measurable business outcomes, including efficiency gains, cost reduction, quality improvements, and cycle-time reduction
- Collaborate cross-functionally with Product, Engineering, and Delivery teams to identify high-impact use cases and ensure successful integration into workflows
- Establish best practices and reusable frameworks for ML, GenAI, and agentic AI development across teams
- Lead technical initiatives and mentor engineers, elevating team capability in AI/ML system design and implementation
- Ensure production readiness and reliability through robust testing, validation, monitoring, and governance of AI systems
- Stay current with emerging AI/ML advancements and evaluate their applicability to business problems
EXPERIENCE AND QUALIFICATIONS
- Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or related field
- 8+ years of experience in machine learning, AI, or data-driven system development
- Proven track record of building and deploying production-grade ML/AI systems at scale
- Strong foundation in statistics, optimization, probability, and experimental design
- Expertise in Python and ML ecosystems (PyTorch, TensorFlow, scikit-learn)
- Hands-on experience with Generative AI / LLMs, including:
- Prompt engineering
- Fine-tuning / adaptation techniques
- Retrieval-Augmented Generation (RAG)
- Evaluation and deployment of LLM-based systems
- Experience designing end-to-end ML pipelines and MLOps workflows, including:
- CI/CD for ML
- Model monitoring and drift detection
- Experiment tracking and versioning
- Experience with cloud platforms (AWS, GCP, Azure) and scalable data/compute systems
PREFERRED SKILLS
- Advanced expertise in deep learning architectures (transformers, sequence models, etc.)
- Experience with agentic AI architectures and orchestration frameworks (e.g., LangChain, Semantic Kernel)
- Familiarity with vector databases, embeddings, and retrieval systems
- Experience with distributed data processing frameworks (e.g., Spark, Ray)
- Understanding of secure, scalable, and governed AI system design (RBAC, data privacy, model governance)
- Experience working in cross-functional environments driving AI adoption in business workflows
RELEVANT EXPERIENCE AND IMPACT
- Delivered AI/ML solutions that were successfully deployed and adopted in production workflows
- Demonstrated measurable impact through reduction in manual effort, operational cost, or cycle time
- Built scalable, reusable AI components or frameworks leveraged across teams
- Partnered effectively across Product, Engineering, and Delivery to accelerate solution development and adoption
