- Company Name
- Intelliswift - An LTTS Company
- Job Title
- AI/ML Engineer
- Job Description
-
**Job Title**
AI/ML Engineer
**Role Summary**
Design, develop, and deploy AI systems that combine generative, narrow, and traditional machine‑learning models for production‑grade applications. Engineer scalable AWS-based infrastructure, data pipelines, and CI/CD workflows, ensuring high performance, reliability, and compliance with data‑privacy regulations.
**Expectations**
- Deliver end‑to‑end AI solutions within project timelines.
- Achieve measurable improvements in model latency, accuracy, and scalability.
- Maintain robust documentation and share knowledge across teams.
**Key Responsibilities**
- Architect and implement AI systems (GenAI, RAG, multi‑agent, protocol‑based) using AWS services (SageMaker, Bedrock, Lambda, Fargate, ECS).
- Build microservices and REST/GraphQL APIs to expose AI models to client applications.
- Design, develop, and maintain data pipelines for training, evaluation, and monitoring with Glue, S3, Step Functions, Kinesis.
- Create automated CI/CD pipelines for model deployment, observability, and monitoring.
- Apply containerization (Docker) and orchestrate workloads on AWS Fargate/ECS.
- Ensure data governance and security compliance (FERPA, GDPR, IAM).
- Collaborate with cross‑functional teams, participate in agile ceremonies, and mentor junior staff.
**Required Skills**
- Strong proficiency in Python, PyTorch/TensorFlow, and NLP libraries.
- Experience with generative AI (e.g., LLM fine‑tuning) and classical ML (regression, classification).
- Deep knowledge of AWS AI/ML services (SageMaker, Bedrock, Lambda, API Gateway, EventBridge).
- Expertise in building and maintaining data pipelines (Glue, Glue ETL, Kinesis).
- CI/CD tools (Jenkins, GitHub Actions, AWS CodePipeline) and IaC (CloudFormation/Terraform).
- Containerization (Docker) and orchestration (ECS/Fargate).
- Familiarity with security best practices (IAM, VPC, encryption) and compliance standards (FERPA, GDPR).
- Strong written and verbal communication, code‑review etiquette, and agile practices.
**Required Education & Certifications**
- Bachelor’s degree in Computer Science, AI/ML, Data Engineering, or related field (Master’s preferred).
- AWS certification (Solutions Architect, Developer) preferred; Professional‑level a plus.
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