Lead AI Engineer

This is a fully remote position, open to applicants in India.

📋 Description

• Design and execute comprehensive AI/ML solutions, encompassing applications based on LLMs.

• Construct RAG pipelines utilizing vector databases and organizational data sources.

• Develop machine learning models with automated processes for training, validation, monitoring, and retraining.

• Create APIs and services to implement AI capabilities throughout the organization.

• Establish ingestion systems for handling multimodal content.

• Formulate transformation pipelines for both structured and unstructured data.

• Integrate AI workflows with enterprise systems, including those for policy, claims, and billing.

• Ensure data quality, traceability, reliability, and governance within AI pipelines.

• Implement CI/CD practices for AI/ML workflows.

• Deploy, monitor, and maintain models in a production environment.

• Oversee model versioning, performance monitoring, and processes for retraining.

• Develop solutions utilizing Amazon SageMaker, AWS Lambda, S3, Glue, EKS, and related AWS services.

• Contribute to the progressive use of AWS Bedrock.

• Implement LLM guardrails for grounding, validation, and safety measures.

• Ensure the secure handling of sensitive data, including personally identifiable information and financial details.

• Build systems that adhere to enterprise governance and compliance standards.

⛳️ Requirements

• Over 10 years of experience in software or data engineering.

• At least 5 years of experience in AI/ML engineering.

• Practical experience in developing production AI/ML systems.

• Familiarity with RAG pipelines, LLMs, or NLP systems.

• Experience with AWS Bedrock or comparable GenAI platforms.

• Proficiency in data pipelines and distributed systems.

• Experience in deploying and managing systems within AWS.

• Working knowledge of MLOps practices, including CI/CD, monitoring, and versioning.

• Experience with vector databases such as Pinecone or Weaviate is preferred.

• Background in regulated industries like insurance, finance, or healthcare is preferred.

• Exposure to microservices and containerized environments, including Docker and Kubernetes is preferred.

🏝️ Benefits

• Commitment to diversity and inclusion.

• Flexible work arrangements based on employee, customer, and business requirements.

• Options for part-time work.

• Ability to work outside standard 9–5 business hours.

• Remote work opportunities.

• Welcome back program for individuals returning after extended health or family-related leaves.

• Opportunities for career advancement.

• Chance to make a meaningful impact.

• A workplace culture that values diverse perspectives and inclusivity.