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:
typedisc:
: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
:Bachelors or Masters 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.
Job Type: Permanent