Data Science AI Engineer, Professional 1

Summary 

Gainwell is seeking LLM Ops Engineers and ML Ops Engineers to join our growing AI/ML team. This role is responsible for developing, deploying, and maintaining scalable infrastructure and pipelines for Machine Learning (ML) models and Large Language Models (LLMs). You will play a critical role in ensuring smooth model lifecycle management, performance monitoring, version control, and compliance while collaborating closely with Data Scientists, DevOps, and

Role Description 

Core LLM Ops Responsibilities:

• Develop and manage scalable deployment strategies specifically tailored for LLMs (GPT, Llama, Claude, etc.).
• Optimize LLM inference performance, including model parallelization, quantization, pruning, and fine-tuning pipelines.
• Integrate prompt management, version control, and retrieval-augmented generation (RAG) pipelines.
• Manage vector databases, embedding stores, and document stores used in conjunction with LLMs.
• Monitor hallucination rates, token usage, and overall cost optimization for LLM APIs or on-prem deployments.
• Continuously monitor models for its performance and ensure alert system in place.
• Ensure compliance with ethical AI practices, privacy regulations, and responsible AI guidelines in LLM workflows.

Core ML Ops Responsibilities:

• Design, build, and maintain robust CI/CD pipelines for ML model training, validation, deployment, and monitoring.
• Implement version control, model registry, and reproducibility strategies for ML models.
• Automate data ingestion, feature engineering, and model retraining workflows.
• Monitor model performance, drift, and ensure proper alerting systems are in place.
• Implement security, compliance, and governance protocols for model deployment.
• Collaborate with Data Scientists to streamline model development and experimentation.

What We’re Looking For 

• Bachelor's or Master's degree or higher in Computer Science, Data Sciences-Machine Learning, Engineering, or related fields.
• Strong experience with ML Ops tools (Kubeflow, ML flow, TFX, Sage Maker, etc.).
• Experience with LLM-specific tools and frameworks ( LangChain, Lang Graph,  LlamaIndex, Hugging Face, OpenAI APIs, Vector DBs like Pinecone, FAISS, Weavite, Chroma DB etc.).
• Solid experience in deploying models in cloud (AWS, Azure, GCP) and on-prem environments.
• Proficient in containerization (Docker, Kubernetes) and CI/CD practices.
• Familiarity with monitoring tools like Prometheus, Grafana, and ML observability platforms.
• Strong coding skills in Python, Bash, and familiarity with infrastructure-as-code tools (Terraform, Helm, etc.).Knowledge of healthcare AI applications and regulatory compliance (HIPAA, CMS) is a plus. 
• Strong skills in Giskard, Deepeval etc.

Qualifications

• Bachelor or Masters or Higher in Computer Sciences, Data Sciences, or any related field
• 3+ years to 7 Years of experience in deploying ML/DL and LLM based solutions in large scale deployment environment or related experience
• Experience with fine-tuning LLMs and serving them in production at scale.
• Knowledge of model compression techniques for LLMs (LoRA, QLoRA, quantization-aware training).
• Experience with distributed systems and high-performance computing for large-scale model serving.

Awareness of AI fairness, explainability, and governance frameworks.

What You Should Expect in This Role 

• Fully Remote Opportunity – Work from anywhere in the U.S.  / India
• Minimal Travel Required – Occasional travel opportunities (0-10%). 
• Opportunity to Work on Cutting-Edge AI Solutions in a mission-driven healthcare technology environment.