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.
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.
Your role in our mission
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/Master's degree in computer science, Engineering, or related fields.
Strong experience with ML Ops tools (Kubeflow, MLflow, TFX, SageMaker, 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.
What you should expect in this role
Fully Remote Opportunity – Work from anywhere in the India
Minimal Travel Required – Occasional travel opportunities (0-10%)
Opportunity to Work on Cutting-Edge AI Solutions in a mission-driven healthcare technology environment
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.

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