AI / Senior AI Engineer
Innovaccer Inc. (company site, opens in a new tab)NoidaOn-siteDirect career pageEst. 40–70 LPA
Posted on 7 Sept
Reach people at Innovaccer Inc.
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Innovaccer Inc. you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Own and build complex AI model systems behind healthcare products, including fine-tuning, model composition, production deployment, and maintaining product-level accuracy.
- Lead post-training and domain adaptation of language models from 1B to 100B+ parameters, including handling multi-node GPU training and evaluating model performance in real clinical contexts.
- Develop and maintain pipelines turning raw, multi-format healthcare data into training-ready datasets and build multi-step agentic AI that reliably performs operational healthcare tasks under latency and cost constraints.
Minimum Requirements
- MS or PhD in Computer Science, Machine Learning, or related quantitative field; exceptional BS candidates with strong research/open-source work considered.
- Strong Python and PyTorch skills with experience fine-tuning open-weight language models and familiarity with training/serving stacks (e.g., HuggingFace, FSDP, DeepSpeed).
- Hands-on multi-node training experience on models with tens of billions of parameters or larger using tens of GPUs, including distributed training strategies.
- Work Experience Required: Senior AI Engineer level requires prior production experience with post-trained models, building model layers behind real product features, and owning data pipelines for training runs.
Ideal Candidate Profile
- Experienced in product-focused AI modeling with ability to scope ambiguous problems into clear plans differentiating research from engineering tasks.
- Demonstrated expertise in handling large-scale, multi-GPU training infrastructure and optimizing model latency/cost in real-world healthcare environments.
- Background in regulated or high-stakes domains with a focus on rigorous evaluation methodology, data-centric research, and production-grade AI system reliability.