Search Jobs

Search by job, company or skills

Senior AI Engineer/Lead AI Engineer

Senior AI Engineer/Lead AI Engineer

Mathco Health Corporation
Early Applicant
  • Posted 10 hours ago
  • Be among the first 10 applicants

Job Description

Designation: Senior/Lead AI Engineer

Senior AI Engineer

Experience: 4 to 6 years

Key Responsibilities

  • Design, build, and maintain scalable AI/ML solutions.
  • Develop GenAI applications using LLMs, embeddings, RAG, and prompt engineering.
  • Work with Python (Pandas, NumPy, FastAPI) and cloud platforms (Azure/AWS/GCP).
  • Collaborate with stakeholders to understand requirements and deliver solutions.
  • Support deployment, optimization, and monitoring of AI applications.

Lead AI Engineer

Experience: 7 to 8+ years

Key Responsibilities

  • Lead and manage a team of AI Engineers.
  • Mentor team members and drive technical excellence.
  • Own solution architecture and end-to-end AI project delivery.
  • Engage with client stakeholders, define roadmaps, and manage project priorities.
  • Handle resource planning, task allocation, and performance management.
  • Foster innovation, collaboration, and capability development within the team.

More Info

Key Skills

GenAI

embeddings

LLMs

prompt engineering

RAG

Similar Jobs

5-7 yrs
Bengaluru, India
Skills:
graph databases , Tensorflow, Pytorch, Gcp, Docker, Azure, Python, Kubernetes, AWS, Hugging Face, Amazon EKS, vector databases, Istio, LLM APIs, Kubernetes Gateway API, AI memory management frameworks
6-8 yrs
Bengaluru, India
Skills:
Python, LangChain, OpenAI API, LangGraph, API integrations
5-7 yrs
Bengaluru, India
Skills:
Exploratory Data Analysis, Sql, Tensorflow, Nosql, Azure ML, Pytorch, XGBoost, Python, Etl, data preprocessing, ML model evaluation, Scikit-learn, MLflow, Vertex AI, Kubeflow, feature engineering
8-10 yrs
Bengaluru, India
Skills:
containerisation with Docker, secrets management, pipeline automation with Azure DevOps, networking and load balancing, relational and non-relational databases, developing production-grade backend services using Python, asynchronous programming frameworks, cloud provisioning frameworks such as Terraform or CloudFormation, infrastructure-as-code, LLM API gateway technologies, container orchestration such as ECS Fargate, identity and access management technologies, observability and monitoring tools, multi-provider integration across major cloud AI platforms