MLOps Databricks Engineer

As a Senior/Lead specializing in Generative AI and Machine Learning Engineering (MLE), you will be at the forefront of AI innovation. You will design, deploy, and operationalize sophisticated modelsincluding Large Language Models (LLMs)—to solve complex business challenges. Collaborating with cross-functional teams, you'll deliver scalable, production-ready AI solutions that drive intelligent automation and spark creativity across industries.

Key Responsibilities:

• Generative AI, NLP & MLE: Design, develop, deploy, and scale cutting-edge applications leveraging Generative AI models (e.g., GPT, LLaMA, Mistral), NLP techniques, and MLE/MLOps best practices.
• Model Customization & Fine-Tuning: Fine-tune and customize LLMs using techniques like LoRA and PEFT to create domain-specific, deployable models aligned with real-world business use cases.
• ML Engineering & Deployment: Build and maintain end-to-end ML pipelines—including data preprocessing, model training, versioning, testing, and deployment—using MLflow, Docker, Kubernetes, and CI/CD methodologies.
• Innovative Problem Solving: Apply advanced AI and ML methodologies to solve practical business problems, delivering measurable impact.
• Scalable AI Solutions: Collaborate with platform and data engineering teams to ensure robust model deployment, monitoring, and retraining in production environments.
• Data-Driven Insights: Analyze structured and unstructured data to uncover trends, optimize model performance, and guide strategic decisions.
• Cross-Functional Collaboration: Partner with Consulting, Engineering, and Platform teams to integrate AI/ML solutions into enterprise architectures and business strategies.
• Client Engagement: Interact directly with clients to understand requirements, present customized AI solutions, and advise on the adoption and operationalization of Generative AI and ML technologies.

Required Skills and Expertise:

• Experience: Hands-on experience with real-world projects in Generative AI and Machine Learning Engineering (MLE/MLOps).
• Generative AI Expertise: Deep expertise in designing and deploying LLM-based solutions using frameworks like HuggingFace, LangChain, Transformers, etc.
• MLE & Production Readiness: Proven track record of building scalable, reliable, production-grade ML models and pipelines.
• Deployment & Best Practices: Hands-on experience with containerization (Docker), orchestration (Kubernetes), model tracking (MLflow), and deployment on cloud platforms (AWS, GCP, Azure).
• Development Proficiency: Strong coding skills in Python, with experience using web frameworks like Django or Flask.
• API & Cloud Architecture: Deep understanding of APIs, microservices, and cloud-native deployment strategies.
• Innovation & Curiosity: A strong passion for staying up to date with the latest advancements in Generative AI, LLMs, and ML engineering.
• Communication: Ability to translate complex technical concepts into clear, business-focused insights and actionable recommendations.