MLOps Engineer 9Core ML + MLOps
At a glance
Mid-level ML Platform & Ops role at Fractal. Mumbai · full-time.
Pay not stated ML Platform & Ops salary
aimlroles summary, based on the employer's posting.
What you'll do
- Automate Databricks ML workflows, from environment setup and model handling to scheduled jobs and monitoring hooks
- Build reusable templates and utilities so teams can run ML experiments consistently and at scale
- Create CI/CD pipelines, then package, version and deploy models in Databricks execution environments
- Turn research notebooks into production pipelines with data scientists, including training, retraining and evaluation workflows
- Integrate embeddings, retrieval-augmented generation, vector databases and LangChain agent workflows into AI solutions
What you bring
- A bachelor's or master's degree in computer science, engineering or a related discipline
- Six to nine years working in ML engineering, MLOps or platform engineering
- Strong practical skills with Databricks, Spark, Python and Scala, including Spark batch and streaming workloads
- Experience with MLflow lifecycle tools and CI/CD platforms such as GitHub Actions, Jenkins or Azure DevOps
- Experience deploying cloud-based ML models and building embedding or LLM components, including LangChain workflows
Who this fits
This is a senior, hands-on MLOps position focused on Databricks and ML delivery. Claims Payment Integrity experience is preferred, while actuarial and forecasting analytics exposure is an added advantage.
From the employer
It's fun to work in a company where people truly BELIEVE in what they are doing!
We're committed to bringing passion and customer focus to the business.
Job DescriptionEL3 – Databricks MLOps Engineer (Contract)
Domain: Claims Payment Integrity | M&R, C&S, E&I Claims (preferred)
Actuarial & Forecasting Analytics Exposure is an Added Advantage
Tech Stack: Databricks, Spark, Python, Scala, Azure, GitHub Actions, Terraform
AI/LLM Capabilities: Embedding Models, LLM Integration, LangChain Agentic Frameworks
Role Summary
The EL3 Databricks MLOps Engineer is a senior hands-on role responsible for enabling end-to-end machine learning lifecycle automation on Databricks. This includes building and maintaining the CI/CD infrastructure, environment configuration, packaging and deploying ML models, supporting reproducible experiments, and ensuring scalable job orchestration for AI/ML workloads, including LLM-based applications.
The role partners closely with Data Scientists, AI/ML Engineers, platform teams, and business stakeholders within Claims Payment Integrity to ensure robust, reliable, and automated ML delivery.
Key Responsibilities
Enable and automate the end-to-end ML lifecycle on Databricks (environment setup, model workflow automation, job scheduling, monitoring hooks).
Build frameworks, templates, and utilities that make ML development and experimentation reproducible and scalable.
Implement CI/CD pipelines using Git, GitHub Actions, Jenkins, Azure DevOps, or similar tools.
Package, version, and deploy ML models into Databricks-managed execution environments.
Set up automated workflows for training, retraining, evaluation, and scheduled job execution.
Support creation and integration of machine learning models including classification, forecasting, anomaly detection, NLP, and PI models.
Enable LLM/GenAI-driven solutions by integrating:
Embedding model generation
RAG architectures
Vector databases
LangChain agentic workflows
Optimize resource usage, runtime configurations, and code execution patterns for ML workloads.
Collaborate with Data Scientists to translate experimental notebooks into production-ready pipelines.
Implement platform-level controls for environment consistency, dependency management, access control, and model versioning.
Support troubleshooting, debugging, and performance improvements for ML workloads.
Document standards, templates, guidelines, and best practices for MLOps teams.
Work cross-functionally with product, engineering, and analytics teams across PI.
Required Qualifications
Bachelor’s/Master’s degree in Computer Science, Engineering, or related field
6–9 years of relevant experience in ML Engineering, MLOps, or platform engineering
Strong hands-on experience with Databricks, Spark (batch/streaming), Python, Scala
Experience enabling ML lifecycle tools such as MLflow (tracking, packaging, model registration)
Strong CI/CD experience using Git, GitHub Actions, Jenkins, or Azure DevOps
Experience deploying AI/ML models into cloud environments (Azure preferred)
Ability to create and integrate embedding models, semantic vectors, and LLM-driven components
Experience with LangChain for agentic workflows and integration of tools/functions
Strong problem-solving, debugging, and collaboration skills
Preferred Qualifications
Experience with Azure OpenAI or OpenAI-compatible LLM APIs
Familiarity with healthcare claims workflows, PI, FWA, provider billing, or pricing
Experience in Agile/Scrum environments
Strong understanding of software engineering best practices, packaging, dependency management
Good-to-Have Data Knowledge
Call Center datasets (member & provider interactions)
Provider RCM datasets (billing, coding, authorizations)
EHR/clinical datasets for cross-domain validation
If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!
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