Machine Learning Engineer

As a Senior Machine Learning Engineer , you will take a hands‑on technical consultant role in designing, developing, and optimising end‑to‑end ML systems that operate at massive scale. You’ll help shape the strategy, own critical components of the ML platform, and mentor junior engineers. Coordinate with the business owner to translate business requirements to tech deliveries. What you’ll do Design, build, and own large-scale, distributed machine learning systems for training, deployment, inference, and monitoring. Collaborate closely with ML Scientists to productize and scale ML models, from experimentation to robust production systems. Lead design discussions and architecture reviews; drive high‑impact engineering decisions for ML platforms and applications. Mentor and coach junior engineers and peers on best practices in ML engineering, system design, and code quality. Develop and maintain reusable components, libraries, and tools to accelerate ML development lifecycle. Proactively identify areas for improvement in model performance, pipeline efficiency, data quality, or platform capabilities. Ensure scalability, observability, and fault‑tolerance across all components of the ML stack. Promote engineering excellence by advocating for best practices in testing, CI/CD, infrastructure‑as‑code, and monitoring. Partner with stakeholders across Data Engineering, Product, Marketing, and Platform teams to align solutions with business goals. Stay up to date on advancements in MLOps, ML frameworks, distributed systems, and apply learnings to improve systems and processes. Who you are 6+ years of experience in software/ML engineering with a proven track record of delivering ML solutions at scale. Strong hands‑on experience with Generative AI and Large Language Models (LLMs), including prompt engineering, fine‑tuning, evaluation, RAG (Retrieval-Augmented Generation), and LLM application development. Experience building and deploying production‑grade AI/LLM solutions using frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, or similar. Deep understanding of Transformer architectures, embedding models, vector databases, and LLM inference optimization techniques. Strong programming skills in modern languages such as Python, Scala, or Java . Deep experience in building and maintaining production‑grade ML pipelines and infrastructure . Expertise in MLOps practices , including model lifecycle management, versioning, monitoring, and CI/CD for ML. Experience with big data ecosystems (e.g., Spark, Hive, Databricks, Delta Lake) and streaming technologies. Proficient in working with ML frameworks like TensorFlow, PyTorch, XGBoost , or similar. Experience working in cloud‑based environments (AWS, GCP, or Azure) and with infrastructure‑as‑code tools. Strong background in data structures, algorithms , and system design for scalable data processing. Hands‑on experience with orchestration tools like Flyte, Airflow, Kubeflow , etc. Proficient in containerization and orchestration technologies like Docker and Kubernetes. Demonstrated ability to lead cross‑functional projects and influence technical direction across teams. Comfortable with automated testing across different layers (unit, integration, functional). Familiarity with advanced ML techniques, including deep learning, NLP, recommendation systems , and generative AI . Excellent written and verbal communication skills with a collaborative mindset. Designing or implementing multi‑agent architectures for autonomous collaboration and decision‑making. Understanding of agent planning, memory, tool use, and self‑reflection mechanisms . #J-18808-Ljbffr