Technical Lead: 8+ yrs (Python, Docker, MLOps, Cloud platforms, LLM)

UniCourt is a leader in making court data more accessible and useful with our Legal Data as a Service (LDaaS). We provide real-time access to court data through our APIs and online app for business development and intelligence, litigation analytics, litigation tracking, case research, investigations, background checks, due diligence, compliance, underwriting, machine learning models, and process automation.

We provide access to court data from state and federal courts to a diverse list of clients, including Fortune 500 companies and AmLaw firms and industries such as legal, insurance, finance, investigations, government, education, nonprofits, and consumers.

UniCourt is a legal technology company focused on using technology to unlock the potential of legal data. We are based in both California and Mangalore, India and our team includes legal professionals, data scientists, physicists, computer engineers, and sales and marketing, professionals.

We are looking for a highly skilled Technical Lead – AI/ML Engineering with a minimum of 8 years of software development and machine learning experience. The candidate will be responsible for architecting, developing, and deploying AI-driven systems with a strong focus on Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and MLOps best practices. The ideal candidate will take ownership of designing scalable AI systems, mentoring a team of developers and ML engineers, and ensuring robust deployment pipelines for production-ready ML services.

Our company creates some of the world’s most cutting-edge software solutions in the legal industry. We solve difficult problems, work on innovative technology, and build world-class platforms for people and enterprises to interact with court records and other public data sets. With some of the best minds in the industry, we’re one of the most sought-after learning and career destinations in the world of legal tech. If you’re looking to work at a company with opportunities to forge your career path in technology, UniCourt is the right place for you.

• LLMs & Generative AI
• 1. Lead the design and deployment of applications using Large Language Models (OpenAI, Claude, Llama, DeepSeek, etc.).
• 2. Build fine-tuning pipelines, prompt engineering frameworks, and parameter-efficient training workflows (LoRA, PEFT, adapters).
• 3. Evaluate model performance and optimize for cost, scalability, and latency in production environments.
• Retrieval-Augmented Generation (RAG)
• 1. Architect and implement RAG pipelines for integrating structured and unstructured knowledge bases into LLMs.
• 2. Optimize embedding generation, vector database usage (Pinecone, Weaviate, FAISS, etc.), and retrieval efficiency.
• 3. Ensure domain adaptation of LLMs for accuracy, contextual relevance, and reduced hallucination.
• MLOps & Model Lifecycle Management
• 1. Establish and enforce end-to-end MLOps workflows including data versioning, model training, testing, deployment, and monitoring.
• 2. Implement CI/CD pipelines for ML models using MLflow, Kubeflow, or similar platforms.
• 3. Drive practices for model monitoring, drift detection, and retraining strategies.
• Software Engineering & Infrastructure
• 1. Build microservices-based ML applications using Python and Docker.
• 2. Collaborate with DevOps teams to optimize containerized deployments on Kubernetes.
• 3. Leverage cloud AI platforms (AWS SageMaker, GCP Vertex AI) for training, deployment, and scaling ML workloads.
• Leadership & Mentorship
• 1. Lead and mentor a team of 5–8 developers/ML engineers.
• 2. Conduct regular code reviews, ML pipeline audits, and architecture validations.
• 3. Drive adoption of AI-assisted development tools (GitHub Copilot, Cursor.io, Windsurf) to boost productivity.
• 4. Collaborate with Product Managers, Engineering Managers, and Data Scientists to translate requirements into deliverables.
• Research & Innovation
• 1. Execute time-bound Proof-of-Concepts (POCs) with open-source ML frameworks and emerging AI technologies.
• 2. Stay up to date with the latest AI research in LLMs, retrieval systems, and generative AI, and evaluate their applicability to production systems.

• Bachelor’s or Master’s degree in Computer Science, AI/ML, or a related field.

• 8+ years of professional experience in Python software development.
• 8+ years of professional experience with LLMs, including fine-tuning, prompt engineering, and deploying both API-based and open-source models.
• Strong expertise in designing and implementing RAG pipelines with vector databases.
• Solid knowledge of MLOps workflows, model monitoring, drift detection, and CI/CD for ML.
• Proficiency with Docker and microservices-based architectures.
• Proven experience managing and mentoring a team of 5+ developers/ML engineers.
• Experience with cloud AI platforms such as AWS SageMaker or GCP Vertex AI (preferred).
• Excellent understanding of ML system performance optimization (latency, throughput, cost efficiency).
• Strong problem-solving, architecture design, and stakeholder collaboration skills.

• Familiarity with distributed training (PyTorch Lightning, Hugging Face Accelerate, Ray).
• Knowledge of data pipelines (Airflow, Prefect, Dagster).
• Hands-on experience with GPU optimization and inference serving frameworks (Triton, ONNX Runtime, TensorRT).