About the Company
Anblicks is committed to driving innovation in the data and AI ecosystem, fostering a collaborative and inclusive culture that empowers our team to lead enterprise-scale transformations.
About the Role
The role involves leading the design and implementation of scalable ML frameworks, optimizing solutions within the Databricks ecosystem, and collaborating with various stakeholders to productionize machine learning use cases.
Responsibilities
- ML Leadership & Architecture
- Lead the design and implementation of scalable ML frameworks on Databricks
- Define and enforce best practices, standards, and governance for ML lifecycle management
- Architect end-to-end ML pipelines covering data ingestion, feature engineering, training, deployment, and monitoring
- Drive platform standardization for model development and operationalization
- Databricks & Platform Engineering
- Build and optimize solutions using Databricks ecosystem: MLflow, Unity Catalog, Workflows, Delta Lake, Mosaic AI
- Develop high-performance Spark-based pipelines for large-scale data processing
- Enable model versioning, experiment tracking, and reproducibility
- Ensure scalability, security, and performance of ML platforms
- MLOps & DevOps Integration
- Implement CI/CD pipelines for ML workflows using GitHub Actions, Jenkins, Terraform, or similar tools
- Automate model deployment, monitoring, and retraining pipelines
- Work with containerization and orchestration tools like Docker and Kubernetes
- Establish observability and monitoring frameworks for ML systems
- Advanced AI / GenAI Enablement (Good to Have)
- Support development of LLM-based and GenAI solutions
- Build RAG pipelines and integrate LLMs into enterprise workflows
- Evaluate and onboard tools like OpenAI, Bedrock, Vertex AI, LangGraph
- Stakeholder Collaboration
- Collaborate with Data Scientists, Data Engineers, and Product Teams to productionize ML use cases
- Translate business requirements into scalable ML solutions
- Provide technical leadership, mentorship, and code reviews
- Drive continuous improvement and innovation in ML practices
Qualifications
- 8+ years of experience in ML Engineering / MLOps
- Strong hands-on experience with Databricks (must-have)
- Expertise in MLflow, Unity Catalog, Workflows, Delta Lake
- Strong programming skills in Python (mandatory)
- Experience with Apache Spark and distributed data processing
- Solid understanding of ML lifecycle and model deployment strategies
- Experience with CI/CD and DevOps practices
- Hands-on experience with Airflow, Kubeflow, or similar orchestration tools
- Experience working with AWS / Azure / GCP cloud platforms
Required Skills
- Databricks Certification (Associate / Professional / Architect)
- Experience with GenAI / LLM ecosystems
- Familiarity with vector databases (Pinecone, ChromaDB, etc.)
- Experience with Docker, Kubernetes, and infrastructure as code (Terraform)
- Exposure to model monitoring and observability tools
Preferred Skills
- Strong ownership mindset with the ability to lead from the front
- Excellent problem-solving and analytical thinking
- Ability to manage multiple priorities in a fast-paced environment
- Strong communication skills with both technical and business stakeholders
- Mentorship experience and team collaboration skills
Pay range and compensation package
Competitive salary based on experience and qualifications.
Equal Opportunity Statement
Anblicks is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
