Generative AI Engineer
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About the Role:-
We are seeking a Generative AI Engineer with 2+ years of experience to build and scale production-ready Agentic Systems using Large Language Models (LLMs). You will work on RAG pipelines, agent workflows, evaluation, and deployment of reliable AI systems.
Key Responsibilities:-
-Build retrieval-augmented generation (RAG) pipelines with embeddings, hybrid search, and reranking
-Implement agent orchestration, tool/function calling, and prompt management
-Develop evaluation, monitoring, and observability for LLM systems
-Ensure AI safety, governance, and data privacy best practices
-Optimize performance using caching, batching, and streaming
-Package solutions as APIs/SDKs and deploy using cloud-native tools
Tech Stack:-
-Languages: Python (primary), TypeScript
-Frameworks: FastAPI/Flask, LangChain/LlamaIndex
-LLMs: OpenAI, Anthropic, Google, Azure OpenAI, AWS Bedrock
-Infra: Docker, CI/CD, Terraform/CDK
-Cloud: AWS / Azure / GCP (any one)
Requirements:-
-2+ years of software or AI engineering experience
-Strong Python and backend development skills
-Hands-on experience with LLMs and cloud deployments
-Understanding of scalable systems and data security
What We Offer:-
-Work on cutting-edge Generative AI products
-High ownership and learning opportunities
-Competitive compensation and growth
Job ID: 136456609
Skills:
Artificial Intelligence, Artificial Intelligence and Machine Learning, Software Engineering, Machine Learning, FastAPI, Python, Flask, Asynchronous programming, Aws, Azure, Docker, Git, Sql, Data Management, MLops, Llm
Skills:
Tensorflow, Nlp, Pytorch, Python, GloVe, Hugging Face, vector databases, Pinecone, LLM architecture, Word2Vec, RAG pipelines, ChromaDB, fine-tuning techniques, APIs from major AI providers, LangChain, embedding preparation, Google, text processing, LLM training methodologies, LoRA, OpenAI, Neural Network fundamentals, QLoRA, sentence transformers
Skills:
S3, Deduplication, ELT, Tensorflow, Nlp, Pytorch, Python, AWS, Matplotlib, Azure ML, Git, Gcp, Databricks, Azure, Etl, scikit-learn, Hugging Face Transformers, standardization, Cleansing, data quality techniques, enrichment, Vertex AI, AI ML libraries, Streamlit, LangChain, LLMs, Plotly, SageMaker, Profiling, Glue
Skills:
Apis, Microservices, Nlp, Python, LangChain, AI development frameworks, embeddings, vector databases, prompt engineering, AI testing methodologies, synthetic data generation, AI application development
Skills:
Tensorflow, Nlp, Pytorch, Python, LangChain, APIs from major AI providers, GloVe, Google, Hugging Face, vector databases, Pinecone, LLM training methodologies, LLM architecture, Word2Vec, LoRA, OpenAI, Neural Network fundamentals, RAG pipelines, ChromaDB, fine-tuning techniques, QLoRA, sentence transformers
