Work Location - Bengaluru
The Role
Join Addepar's innovative data and intelligence initiatives! We're seeking a Staff Engineer to craft and build an AI/ML Ops platform from the ground up, enabling our teams to scale AI/ML solutions efficiently!
What You'll Do
Build and scale a comprehensive platform, accelerating AI adoption across teams.
Collaborate with product managers and engineers to define requirements, and architect solutions for sophisticated data and workflow challenges.
Use core Addepar systems like the Data Lakehouse to advise and strategize Ops and Data Governance infrastructure.
Simplify processes by promoting strategic data architecture and optimized workflows.
Implement data governance strategies to enhance the value of our data.
Who You Are
8+ years of overall software engineering experience
6+ years of relevant work experience that shows proficiency in platform development, particularly in enabling Machine Learning and AI outcomes.
Proficient in one or more cloud platforms (AWS, GCP, Azure), with hands-on experience in cloud infrastructure and handling machine learning workloads.
Strong expertise in CI/CD for ML pipelines—building, automating, and optimizing the pipeline lifecycle.
Experienced with containerization (Docker, Kubernetes) to deploy models at scale.
Skilled in monitoring and observability tools like FiddlerAI, Datadog, and Grafana to track model performance, data drift, and system health.
Knowledge of Infrastructure as Code tools (Terraform, Ansible, CloudFormation) for consistent, scalable environment setups.
Familiar with ML lifecycle management (Kubeflow, MLflow, TFX) to manage experiments, model versioning, and deployment.
Experienced in securing ML systems and ensuring compliance with regulations (e.g., GDPR).
A collaborative, low-ego problem-solver who takes ownership and delivers results.
Experience with Data Governance initiatives
Must have experience building MLOps Platform
Bonus points for:
Experience with LLM frameworks such as LangChain, Llama Index, or Semantic Kernel and LLM Observability.
Experience in handling large-scale datasets and high velocity data streams.
Proficiency with ML frameworks (TensorFlow, PyTorch).
Familiarity with the financial domain.
PySpark and Databricks experience (or similar technologies with a willingness to cross-train).