We are looking for a Senior ML Engineer.
As a Senior ML Engineer , you will lead the design, deployment, and maintenance of production-ready machine learning systems that power legal insights at scale. Youll work across disciplinesintegrating LLMs, building pipelines, and optimizing infrastructureto deliver real-world impact.
Youll gain full ownership over the systems you build, from selecting the tools and platforms to deploying services in production. Youll work closely with DevOps and Data Engineering to ship robust solutions that drive meaningful change in the world.
Responsibilities :
Design and implement production-grade ML systems, including APIs, batch jobs, and streaming pipelines using Databricks, MLflow, AWS/GCP.
Build and manage ML infrastructure, including data pipelines, model training, deployment, and monitoring.
Develop and maintain end-to-end ML/LLM pipelinesfrom data ingestion and labeling to synthetic data generation, model registry, and rollout.
Own and improve MLOps practices: automated testing, CI/CD, monitoring, alerting, and model governance.
Write clean, maintainable Python code and uphold best practices in engineering and documentation.
Collaborate with DevOps and Data Engineering teams to scale systems and improve performance.
Research and recommend the best tools, platforms, and practices to support ML at scale.
As a Senior ML Engineer , you will lead the design, deployment, and maintenance of production-ready machine learning systems that power legal insights at scale. Youll work across disciplinesintegrating LLMs, building pipelines, and optimizing infrastructureto deliver real-world impact.
Youll gain full ownership over the systems you build, from selecting the tools and platforms to deploying services in production. Youll work closely with DevOps and Data Engineering to ship robust solutions that drive meaningful change in the world.
Responsibilities :
Design and implement production-grade ML systems, including APIs, batch jobs, and streaming pipelines using Databricks, MLflow, AWS/GCP.
Build and manage ML infrastructure, including data pipelines, model training, deployment, and monitoring.
Develop and maintain end-to-end ML/LLM pipelinesfrom data ingestion and labeling to synthetic data generation, model registry, and rollout.
Own and improve MLOps practices: automated testing, CI/CD, monitoring, alerting, and model governance.
Write clean, maintainable Python code and uphold best practices in engineering and documentation.
Collaborate with DevOps and Data Engineering teams to scale systems and improve performance.
Research and recommend the best tools, platforms, and practices to support ML at scale.
Requirements:
BSc in Computer Science or a related field.
6+ years of experience building and deploying ML systems in production environments.
Proficiency in Python and production frameworks like FastAPI, Databricks, SageMaker, and MLflow.
Proven track record in deploying and maintaining ML/LLM services (APIs, microservices, serverless, or containerized).
Strong understanding of software engineering fundamentals: object-oriented design, testing, version control, CI/CD, and performance optimization.
Experience working with agentic workflows or LLM-based agents.
Ability to work independently and break down complex, ambiguous problems into structured solutions.
Strong communication skillsable to explain technical concepts to both technical and non-technical stakeholders.
Advantages:
Hands-on experience with Kubernetes, Airflow, Spark, ArgoCD, and Docker.
Experience working with databases such as Elasticsearch, vector databases, PostgreSQL, and SQL.
Experience fine-tuning or integrating open-source LLMs in production environments (e.g., RAG, LoRA, agent frameworks).
Contributions to open-source ML or MLOps projects.
BSc in Computer Science or a related field.
6+ years of experience building and deploying ML systems in production environments.
Proficiency in Python and production frameworks like FastAPI, Databricks, SageMaker, and MLflow.
Proven track record in deploying and maintaining ML/LLM services (APIs, microservices, serverless, or containerized).
Strong understanding of software engineering fundamentals: object-oriented design, testing, version control, CI/CD, and performance optimization.
Experience working with agentic workflows or LLM-based agents.
Ability to work independently and break down complex, ambiguous problems into structured solutions.
Strong communication skillsable to explain technical concepts to both technical and non-technical stakeholders.
Advantages:
Hands-on experience with Kubernetes, Airflow, Spark, ArgoCD, and Docker.
Experience working with databases such as Elasticsearch, vector databases, PostgreSQL, and SQL.
Experience fine-tuning or integrating open-source LLMs in production environments (e.g., RAG, LoRA, agent frameworks).
Contributions to open-source ML or MLOps projects.
This position is open to all candidates.
