Technical Lead

Key Responsibilities

Design, build, and deploy scalable ML/DL models for NLP, classification, recommendation, summarization, and text generation tasks.

Fine-tune Large Language Models (LLMs) using techniques such as LoRA, QLoRA, PEFT, and parameter-efficient training.

Build GenAI-powered solutions for RAG, document understanding, chatbot systems, intelligent agents, and decision-support tools.

Implement and orchestrate Agentic AI systems, including tool calling, memory, planning, reflection, and chaining workflows.

Develop robust APIs and microservices to expose AI/ML models to internal and external applications.

Integrate and manage vector databases (e.g., FAISS, Pinecone, Weaviate, Milvus) for semantic search, retrieval, and embedding stores.

Work with databases, including both SQL and NoSQL systems, to handle structured and unstructured data efficiently.

Utilize cloud platforms (AWS, Azure, GCP) for scalable model training, inference, and MLOps pipelines (e.g., SageMaker, Vertex AI, Azure ML).

Optimize model performance and cost through profiling, benchmarking, and cloud-native acceleration.

Collaborate with data scientists, product managers, and backend engineers to embed AI into real-world applications.

Stay current with AI research, tools, and community innovations (e.g., OpenAI, Hugging Face, Meta AI, etc.).

Required Qualifications

Bachelor’s or master’s degree in computer science, Artificial Intelligence, Data Science, or related field.

8-12 years of hands-on experience in machine learning and deep learning, including model deployment and tuning.

Proficient in Python and ML libraries such as PyTorch, TensorFlow, Scikit-learn.

Strong understanding of NLP techniques and experience with Transformer-based models.

Hands-on experience with Generative AI and Foundational Models (e.g., GPT, Claude, LLaMA, Mistral, PaLM).

Experience with API development using FastAPI, Flask, or similar frameworks.

Familiarity with SQL and NoSQL databases, understanding their role in supporting AI/ML pipelines and real-time applications.

Experience with vector databases for semantic search and LLM applications.

Cloud experience with at least one major platform (AWS preferred, Azure, or GCP).

Familiarity with containerization (Docker), version control (Git), and ML lifecycle tools (e.g., MLflow, DVC).

Exposure to Agentic AI systems, autonomous agents, and tool use frameworks such as LangChain or Autogen.

Emids is a leading provider of digital transformation solutions to the healthcare industry, serving payers, providers, life sciences, and technology firms. Headquartered in Nashville, Emids helps bridge critical gaps in providing accessible, affordable, and high-quality healthcare by providing digital transformation services, custom application development, data engineering, business intelligence solutions, and specialized consulting services to all parts of the healthcare ecosystem. With nearly 2000 plus professionals globally, Emids leverages strong domain expertise in healthcare-specific platforms, regulations, and standards to provide tailored, cutting-edge solutions and services to its clients.

At Emids, we are guided by our core values of "Trusted guides", "Impact obsessed" and " Inclusive innovators". We expect all Emidsians, including our candidates and partners, to embody these values in their day-to-day work. We're not scared of differences. It's how we break new ground. As we scale and elevate the experience of our clients in the healthcare and life sciences space and ultimately have an impact on every patient from every walk of life, the team we build must reflect the diversity that we serve. Together, we've built and will continue to grow, a diverse and inclusive culture where everyone has a seat at the table and the space to be their most authentic self. Emids believes in being an Equal Opportunity Employer and we support, celebrate, and cherish all the things that make our teammates who they are.