Senior AI/ML Engineer

Space Inventive; Hyderabad; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Space Inventive in Hyderabad, India, seeks a Senior AI/ML Engineer with hands-on GenAI, LLMs, and AWS Bedrock to design, develop, and deploy AI-driven solutions.

You will collaborate with data scientists and cloud engineers to build scalable AI applications, implement MLOps, and ensure governance, security, and responsible AI in production.

The role requires 4+ years in ML/AI, strong Python skills, and experience with vector databases, embeddings, and modern ML frameworks.

Qualifications

• 4+ years of ML/AI experience including GenAI/LLMs.
• Hands-on AWS Bedrock and AWS AI/ML services.
• Proficient in Python and prompt engineering.
• Experience with RAG, embeddings, and vector databases.
• Familiar with LangChain, LlamaIndex, or similar GenAI frameworks.
• Strong knowledge of PyTorch, TensorFlow, Scikit-learn.
• Experience building REST APIs with FastAPI or Flask.
• Knowledge of ML lifecycle, evaluation, and deployment.

Responsibilities

• Design, develop, and deploy AI/ML solutions using GenAI tech.
• Build GenAI apps with Bedrock and foundation models.
• Develop RAG architectures for enterprise use cases.
• Fine-tune and optimize LLMs.
• Create scalable AI services and APIs in Python.
• Develop data pipelines for model training and inference.
• Integrate vector databases for semantic search.
• Collaborate with cross-functional teams.
• Implement MLOps, model versioning, monitoring.
• Ensure security, governance and responsible AI practices.
• Stay updated with AI advancements.

Skills

Python programming

GenAI & LLMs

MLOps & deployment

Tools

AWS Bedrock

LangChain

PyTorch

TensorFlow

Scikit-learn

FastAPI/Flask

SageMaker

OpenSearch/FAISS/Weaviate

REST API development

Job description

Experience: 4+ Years

About the role :

We are seeking a highly skilled Senior AI/ML Engineer with hands-on experience in Generative AI, Large Language Models (LLMs), AWS Bedrock, and Machine Learning Engineering. The ideal candidate will be responsible for designing, developing, and deploying AI-driven solutions leveraging cutting-edge GenAI technologies and AWS cloud services. You will work closely with data scientists, product teams, and cloud engineers to build scalable AI applications and intelligent automation solutions.

Key Responsibilities :

• Design, develop, and deploy AI/ML solutions using modern machine learning and Generative AI technologies.
• Build and implement GenAI applications using Amazon Bedrock, foundation models (Claude, Llama, Titan, etc.), and prompt engineering techniques.
• Develop Retrieval-Augmented Generation (RAG) architectures for enterprise AI use cases.
• Fine-tune, evaluate, and optimize Large Language Models (LLMs).
• Create scalable AI services and APIs using Python and cloud-native architectures.
• Develop data pipelines for AI model training, inference, and monitoring.
• Integrate vector databases and embedding models for semantic search and knowledge retrieval.
• Collaborate with cross-functional teams to translate business requirements into AI solutions.
• Implement MLOps best practices, model versioning, monitoring, and deployment automation.
• Ensure security, governance, and responsible AI practices in production environments.
• Stay updated with advancements in AI, LLMs, GenAI frameworks, and AWS services.

Required Skills & Experience

• 4+ Years of experience in Machine Learning, Artificial Intelligence, or Data Science projects.
• Hands-on experience in Generative AI/LLM-based application development.
• Strong programming skills in Python.
• Hands-on experience with AWS Bedrock and AWS AI/ML services.
• Experience in Prompt Engineering, RAG, embeddings, and vector databases.
• Strong understanding of LLMs, Foundation Models, Transformers, and NLP concepts.
• Experience with LangChain, LangGraph, LlamaIndex, AWS Strands, or similar GenAI frameworks.
• Knowledge of PyTorch, TensorFlow, Scikit-learn.
• Experience with AWS Lambda, ECS/EKS, S3, API Gateway, SageMaker, CloudWatch.
• Familiarity with vector databases such as FAISS, ChromaDB, Weaviate, or OpenSearch.
• Experience building REST APIs using FastAPI or Flask.
• Strong understanding of model evaluation, performance optimization, and ML lifecycle management.