- Company Name
- Intelliswift - An LTTS Company
- Job Title
- Generative AI Engineer
- Job Description
-
**Job Title:** Generative AI Engineer
**Role Summary:**
Design, develop, and productionalize generative AI and agentic systems using large language models (LLMs), Retrieval‑Augmented Generation (RAG), and Model Context Protocol (MCP). Collaborate with product, data science, and architecture teams to deliver scalable, secure AI/ML solutions on cloud platforms.
**Expectations:**
- 5+ years of professional experience in software engineering and production‑grade machine learning.
- Proven expertise in Python development, LLM/Generative AI, and agentic system design.
- Strong track record of building, deploying, and maintaining end‑to‑end AI/ML pipelines (batch & real‑time).
- Ability to work cross‑functionally, communicate complex concepts clearly, and manage technical dependencies under pressure.
**Key Responsibilities:**
- Develop AI/ML solutions using agentic coding tools (e.g., Claude Code, GitHub Copilot).
- Architect, build, and maintain scalable systems following security‑first design patterns.
- Implement end‑to‑end pipelines with monitoring, logging, automated testing, performance testing, and A/B testing.
- Optimize token and compute costs while ensuring delivery value.
- Collaborate with product managers, enterprise architects, ML engineers, and data scientists to define and ship new capabilities.
- Write clean, efficient code for iterative, continuous‑release environments; conduct code reviews and enforce best‑practice standards.
- Integrate AI/ML models into production products and APIs; ensure reliability and observability.
- Provide technical guidance and documentation for AI/ML components and workflows.
**Required Skills:**
- Programming: Python (expert); familiarity with Java or Scala.
- AI/ML: LLMs, Generative AI, RAG architecture, AI agents, Model Context Protocol (MCP).
- MLOps/LLMOps: CI/CD, automated testing, monitoring, token‑cost optimization.
- Deep Learning Frameworks: PyTorch or TensorFlow.
- Cloud Platforms: AWS or Microsoft Azure (design, deploy, administer scalable services).
- Data Platforms: Databricks, Spark, Kafka, relational SQL and NoSQL databases.
- Software Engineering: OOP, design patterns, API development, distributed systems, service‑oriented architecture.
- Tools: Version control (Git), workflow management, unit testing, code review processes.
**Required Education & Certifications:**
- Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field (Master’s preferred).
- Relevant certifications (e.g., AWS Certified Solutions Architect, Microsoft Azure AI Engineer, or equivalent) are a plus but not mandatory.
San francisco bay, United states
Hybrid
Mid level
12-03-2026