Ai Ml Engineer

Role & responsibilities We are looking for a Software Engineer with US Healthcare background with 3-6 years of Programming experience in AI, LLM, FastAPI, Python, Snowflake, GenAI & CICD. Technical Skills Advanced proficiency in Python with expertise in data science libraries (NumPy, Pandas, scikit-learn) and deep learning frameworks (PyTorch, TensorFlow).Good to have knowledge on CICD Pipelines creation & AirflowExperience with LLM frameworks such as Hugging Face Transformers and LangChain, including prompt engineering and fine-tuning techniques.Hands-on with libraries such as AutoGen, CrewAI, and LangGraph, and skilled in orchestrating agent workflows using LLM-based planning, retrieval, and action chaining.Knowledge of RAG pipelines and semantic search using vector databases and retrieval-augmented generation techniquesHands-on experience with model optimization techniques including quantization (GPTQ, AWQ), pruning, and distillation for efficient deployment.Proficiency in multimodal AI development, integrating text, vision, and audio using models like CLIP, BLIP, Whisper, and LLaVA.LLM Infrastructure & Deployment: Skilled in serving models using FastAPI, managing vector databases (FAISS, Pinecone, Chroma), and building scalable inference pipelines.Software Engineering & Development: Strong coding practices with Python, and experience in microservices, test-driven development, and concurrencyDevOps & Infrastructure: Experience with CI/CD pipelines (GitHub Actions, Jenkins), and container orchestration (Kubernetes, Helm).Cloud Optimization: Familiarity with deploying AI workloads on AWS, Azure, or GCP using tools like SageMakerVersion control and experiment tracking using Git, MLflow, and other MLOps tools for reproducibility and collaboration.Lead development of scalable data pipelines and ETL/ELT workflows using Snowflake, ensuring performance, security, and cloud integration (AWS/Azure/GCP). Domain Expertise Healthcare AI Applications: Understanding of healthcare-specific data modalities, privacy constraints, and domain adaptation for clinical and operational use cases.Evaluation Methodologies: Proficiency in designing benchmarks, conducting human evaluations, and applying automated metrics for model performance and safety.Mathematical Foundations: Strong grasp of linear algebra, probability, optimization theory, and information theory relevant to deep learning and model design.Research Methodology: Experience in experimental design, reproducibility, statistical analysis, and peer-reviewed publication processes. Professional Competencies Strong problem-solving and analytical skills, with the ability to translate complex AI concepts into scalable engineering solutions.Ability to rapidly prototype and iterate on GenAI and LLM-based applications, balancing innovation with performance and reliability.Effective collaboration across cross-functional teams, including data scientists, researchers, and product stakeholders, to deliver impactful AI solutions.Clear and concise communication skills, capable of presenting technical ideas to both technical and non-technical audiences.Commitment to engineering excellence, including writing clean, maintainable code, conducting thorough code reviews, and following best practices in software development.Proactive learning mindset, staying current with emerging trends in AI, GenAI, and agentic systems, and applying them to real-world problems.Experience in mentoring and knowledge sharing, supporting junior engineers and contributing to team growth and capability building.Ownership and accountability in delivering high-quality solutions under tight deadlines and evolving requirements.Focus on reproducibility and reliability, using tools like Git, MLflow, and CI/CD pipelines to ensure consistent experimentation and deployment.Ethical and responsible AI development, with awareness of safety, fairness, and privacy considerations in model design and deployment .