Lead I - ML Engineering

Primary Skills: GenAI, AI/ML, Agentic AI Description:  Sr Software Engineer III - GenAI, AI/ML, Agentic AI JOB RESPONSIBILITY Build and deploy scalable LLM, RAG, and agent-based systems Architect LLM inference and deployment pipelines Optimize models for efficient and cost-effective production Collaborate with data science, research, and product teams Ensure clean code, testing, reproducibility, and CI/CD Mentor junior engineers and drive engineering best practices Ensure ethical, secure, and responsible AI development Operate container orchestration platforms like Kubernetes scalable workload deployments. Stay up-to-date with industry trends and advancements in AI technologies Contribute to creating specialized AI solutions in healthcare Collaborate closely with stakeholders to develop innovative and effective natural language processing solutions, specializing in text classification, sentiment analysis, and topic modeling. QUALIFICATION Bachelor’s or Master’s degree in computer science, Mathematics or Statistics, Computational linguistics, Engineering, or a related field. EXPERIENCE 5+ years of experience in data engineering / software engineering roles. Advanced Python with strong fundamentals in NumPy, Pandas Hands-on with LLM frameworks: Hugging Face Transformers, LangChain (prompting & fine-tuning) Strong experience with Agentic AI frameworks: AutoGen, CrewAI, LangGraph Expertise in RAG pipelines, semantic search, vector databases Ability to rapidly prototype and productionize GenAI and Agentic AI solutions. SKILLS AND COMPETENCIES Technical Skills: Advanced proficiency in Python with expertise in data science libraries (NumPy, Pandas) and deep learning frameworks (PyTorch, TensorFlow) Extensive experience with LLM frameworks (Hugging Face Transformers, LangChain) and prompt engineering techniques Experience with big data processing using Spark for large-scale data analytics Version control and experiment tracking using Git and MLflow Software Engineering & Development: Advanced proficiency in Python, familiarity with Go or Rust, expertise in microservices, test-driven development, and concurrency processing. DevOps & Infrastructure: Experience with Infrastructure as Code (Terraform, CloudFormation), CI/CD pipelines (GitHub Actions, Jenkins), and container orchestration (Kubernetes) with Helm and service mesh implementations. LLM Infrastructure & Deployment: Proficiency in LLM serving platforms such as vLLM and FastAPI, model quantization techniques, and vector database management. MLOps & Deployment: Utilization of containerization strategies for ML workloads, experience with model serving tools like TorchServe or TF Serving, and automated model retraining. Cloud & Infrastructure: Strong grasp of advanced cloud services . LLM Project Experience: Expertise in developing chatbots, recommendation systems, translation services, and optimizing LLMs for performance and security. Develop and maintain microservice architecture and API management solutions. Design and build scalable LLM inference architectures, employing GPU memory optimization techniques and model quantization for efficient deployment Develop and manage data warehousing solutions using Snowflake aligned with business requirements Utilize AWS services such as S3, Athena, RDS, Lambda, Glue, Step Functions, EMR, and CloudWatch for data engineering and pipeline orchestration Work with diverse data formats including JSON, Avro, and Parquet for structured and semi-structured data processing General Skills: Python, SQL, knowledge of machine learning frameworks (Hugging Face, TensorFlow, PyTorch), and experience with cloud platforms like AWS. Domain Expertise: Strong mathematical foundation in statistics, probability, linear algebra, and optimization Deep understanding of ML and LLM development lifecycle, including fine-tuning and evaluation Expertise in feature engineering, embedding optimization, and dimensionality reduction Advanced knowledge of experimental design, and statistical hypothesis testing Understanding of MLOps practices for model deployment and monitoring Professional Competencies: Strong analytical thinking with ability to solve complex AI challenges Excellent communication skills for presenting technical findings to diverse audiences Strong collaboration abilities for working with cross-functional teams Dedication to staying current with latest AI research and best practices