AI Engineer

Maveric; Chennai, Bengaluru; Vor Ort; INR 1.200.000 - 2.400.000; Vollzeit

Zusammenfassung

Maveric in Chennai is seeking passionate AI Engineers to design, develop and deploy AI/GenAI solutions using Python/Java, LLMs, embeddings and RAG. You will build scalable AI services, implement guardrails, and collaborate with product and data teams.

Responsibilities include creating prompt frameworks, MLOps, vector search, and cloud deployments on Azure/AWS/GCP, with a focus on production-grade, secure and explainable AI for enterprise applications.

Qualifikationen

• Strong Python/Java development experience.
• Fundamentals of ML and AI.
• Knowledge of LLMs and Generative AI concepts.
• REST APIs and microservices development.
• SQL and data processing.
• Exposure to cloud platforms (Azure/AWS/GCP).
• Git and software development practices.
• Exposure to LangChain or OpenAI APIs.
• Understanding of RAG concepts and embeddings.

Aufgaben

• Design, develop, and deploy AI/ML and Generative AI solutions.
• Build applications leveraging LLMs, embeddings, transformers, and Retrieval-Augmented Generation (RAG).
• Develop AI-powered services and APIs using Python and/or Java.
• Create prompt engineering frameworks and optimize AI model performance.
• Implement semantic search, vector retrieval, and conversational AI solutions.

Kenntnisse

Python

Java

ML fundamentals

Generative AI

LLMs

REST APIs

SQL

Cloud platforms

Git

MLOps

Tools

LangChain

LangGraph

CrewAI

LangSmith

LLM as a Judge

RAGAS

DeepEval

Pinecone

FAISS

Chroma

Weaviate

Azure AI Search

MLflow

Azure ML

Kubeflow

Databricks

Docker

Kubernetes

Jobbeschreibung

Role Overview

• We are seeking passionate AI Engineers with expertise in Python, Machine Learning, Generative AI, LLMs, and AI platform engineering to build next-generation AI-powered solutions.
• The role involves designing, developing, deploying, and optimizing AI/GenAI applications, integrating Large Language Models (LLMs), implementing RAG architectures, and building scalable AI services for enterprise applications.
• Candidates should possess strong software engineering fundamentals and hands-on experience in building production-grade AI solutions, working across model development, deployment, MLOps, orchestration frameworks, and cloud-native environments.

Key Responsibilities

AI & Generative AI Development

• Design, develop, and deploy AI/ML and Generative AI solutions.
• Build applications leveraging LLMs, embeddings, transformers, and Retrieval-Augmented Generation (RAG).
• Develop AI-powered services and APIs using Python and/or Java.
• Create prompt engineering frameworks and optimize AI model performance.
• Implement semantic search, vector retrieval, and conversational AI solutions.

AI Governance & Responsible AI

• Implement AI guardrails and safety mechanisms.
• Support explainability, compliance, and enterprise AI governance.
• Establish evaluation and benchmarking frameworks for AI models.
• Ensure secure and ethical use of AI technologies.

Collaboration & Leadership

• Work closely with Product, Engineering, Business, and Data teams.
• Participate in solution design, POCs, and technical discussions.
• Conduct code reviews and ensure engineering best practices.
• (Senior Levels) Mentor junior engineers and lead technical initiatives.

Required Skills by Level

• L1/L2 AI Engineer (1-3 Years)

Technical Skills

• Python or Java programming.
• Fundamentals of Machine Learning and AI.
• Basic understanding of LLMs and Generative AI concepts.
• REST APIs and microservices development.
• SQL and data processing.
• Exposure to cloud platforms (Azure/AWS/GCP).
• Knowledge of Git and software development practices.

Preferred

• Exposure to LangChain or OpenAI APIs.
• Understanding of RAG concepts and embeddings.
• Basic Docker knowledge.
• L3/L4 AI Engineer (3-6 Years)

Technical Skills

• Strong Python development experience.
• LLM Evaluation & Observability Exposure to tools such as LangSmith, LLM as a Judge, RAGAS, DeepEval or equivalent.
• Hands-on experience with LLMs, Generative AI, and NLP.
• Experience building RAG solutions and vector search applications.
• Knowledge of LangChain, LangGraph, CrewAI, or similar frameworks.
• Experience with vector databases such as Pinecone, FAISS, Chroma, Weaviate, or Azure AI Search.
• MLOps and model deployment experience.
• Cloud deployment experience on Azure, AWS, or GCP.
• Containerization using Docker and Kubernetes.

Preferred

• Experience with MLflow, Azure ML, Kubeflow, or Databricks.
• Model monitoring and observability.
• Prompt engineering and workflow orchestration.