Senior Machine Learning Engineer

Data/ML Engineer:

The machine learning engineer role focuses on building and operating production-grade, cloud-native machine learning and LLM-powered systems for our Digital’s Intelligence Cloud, an AI-powered SaaS platform running on AWS.

Successful candidates will have strong Python-based machine learning and API engineering skills, with a proven history of shipping well-tested, production-grade systems. Strong working knowledge of AWS and CI/CD pipelines is required, along with hands-on exposure to Docker and Kubernetes. Experience engineering, deploying, monitoring, and operating ML and LLM-based systems using MLflow (mandatory) and modern ML frameworks is required.

Experience Level:

Experienced candidates preferred with 4–5 years of overall software engineering experience, including at least 2 years shipping production-grade Python systems.

Core Skills:  

• Self-driven with strong ownership mindset; comfortable working under ambiguity and evolving requirements
• Strong collaboration skills, working with data scientists, backend engineers, and architects

Required Skills:        

• Strong production-grade Python skills with ability to write clean, modular, testable code.
• Strong API engineering skills, including development of RESTful APIs using FastAPI and/or Flask.
• Hands-on experience building APIs backed by Elasticsearch indexes, including search and retrieval workflows.
• Experience delivering Python services using CI/CD pipelines, with strong coding standards, automated testing, and version control.
• Strong understanding of build, test, release, and packaging practices for Python applications.
• Strong practical understanding of machine learning concepts, including model training, validation, and evaluation, with hands-on experience engineering, deploying, scaling, and operating models built by data scientists in production environments.
• Mandatory experience with MLflow for experiment tracking, model versioning, and lifecycle management
• Good understanding of large language models (LLMs) and how inference APIs work, with hands-on experience using AWS Bedrock, OpenAI, or Hugging Face APIs
• Exposure to agentic AI systems (e.g., multi-step reasoning, tool usage, orchestration, memory) is required; candidates are expected to be able to productionize and operate such systems
• Working knowledge of LLM orchestration frameworks such as LangChain/Langraph
• Hands-on exposure to Docker and Kubernetes for deploying and operating ML and LLM services
• Working knowledge of Apache Spark for distributed data processing
• Experience with data engineering and ETL workflows to prepare datasets for machine learning
• Required working knowledge of common ML and data processing libraries such as Pandas, NumPy, Scikit-Learn, TensorFlow / Keras or PyTorch

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• Knowledge of a strongly typed language such as Java, C# or Rust in addition to Python.

Core responsibilities:

• Work with globally distributed data science, data engineering, frontend, and backend teams to deliver ML and LLM systems in production
• Design, develop, and deploy backend APIs for ML and LLM inference using Python
• Collaborate closely with data scientists while owning productionization, deployment, and operational stability of ML and LLM services
• Design and implement agentic AI systems, including multi-step workflows, tool invocation, orchestration, and production-readiness, in collaboration with MLOps teams
• Write and maintain production-grade Python code supporting ML, LLM, and search-driven workloads on AWS
• Design systems with attention to inference latency, scalability, reliability, and operational cost
• Ensure strong unit test coverage and support QA teams in building automated test strategies
• Maintain clear, current technical documentation for owned systems
• Deploy ML models and LLM services as RESTful APIs and/or event-driven services
• Contribute to model monitoring, including experiment tracking, inference logging, metrics, and performance analysis

Job Location:

Bangalore (Remote)