Python Developer - GenAI / AI/ML Engineer

About the Role\nWe are seeking a Python Developer with strong backend engineering expertise and hands-on exposure to Generative AI, Machine Learning, and Deep Learning to design, build, and scale AI-driven applications.\nThe role involves developing production-grade AI solutions leveraging Large Language Models (LLMs), deep learning models, and cloud AI services across cloud or on-premise environments.\nYou will be responsible for building high-performance backend services, integrating advanced AI/ML models, and enabling scalable API-driven platforms.\nThe ideal candidate should have experience in building LLM-powered systems, implementing Agentic AI workflows,\nand applying AI-first approaches to solve business problems.\nYou will work closely with cross-functional teams to deliver reliable, scalable, and secure AI solutions integrated into enterprise systems.\n\nKey Responsibilities\nDesign, develop, and integrate LLM-based solutions (e.g., OpenAI GPT, LLaMA, HuggingFace models) into enterprise products and workflows\nImplement Retrieval-Augmented Generation (RAG), prompt engineering, embeddings, chunking strategies, and fine-tuning for business use cases\nDevelop APIs and integration layers to seamlessly connect AI models with frontend and backend systems\nBuild and maintain scalable backend applications using Python with microservices architecture\nDesign and implement RESTful APIs using frameworks such as FastAPI (mandatory), Flask, or Django\nDevelop Agentic AI workflows including multi-agent coordination, tool/function calling, memory handling, and workflow orchestration\nIntegrate AI models into applications using APIs and ensure secure and efficient communication across systems\nCollaborate effectively with frontend (Flutter) and backend (Node.js/Python) teams for smooth AI feature deployment\nTest, debug, and manage API integrations using tools like cURL and other debugging mechanisms\nBuild and deploy AI services on cloud platforms using AWS services such as Lambda, S3, API Gateway, EC2, ECS/EKS, DynamoDB, and RDS\nLeverage Amazon Bedrock and SageMaker for model deployment, orchestration, and scaling\nDevelop and integrate machine learning and deep learning models using frameworks such as TensorFlow, PyTorch, and scikit-learn\nWork on NLP, classification, regression, clustering, anomaly detection, and time-series modeling problems\nBuild scalable data pipelines for data processing, training, validation, and inference\nEnsure systems are secure, scalable, cost-optimized, and production-ready with proper monitoring and observability\nImplement DevOps and MLOps best practices including CI/CD, model versioning, logging, and performance tracking\nCollaborate with product teams and stakeholders to translate business requirements into AI-driven solutions\nContribute to architecture design, innovation, and continuous improvement of AI platforms\n\nRequired Technical Skills:\n\nLLM & AI Integration (Mandatory – Hands-on)\nStrong hands-on experience working with LLMs and Generative AI systems\nExperience integrating LLMs such as OpenAI GPT, LLaMA, HuggingFace models into real-world applications\nExperience with frameworks such as LangChain, LlamaIndex, LangGraph, ADK, or similar\nHands-on experience with vector databases such as Pinecone, Weaviate, Milvus, FAISS, or OpenSearch\nProven ability to build and deploy RAG pipelines, embeddings-based retrieval systems, and prompt engineering workflows\nExperience integrating AI models via APIs into live production systems\n\nProgramming & Frameworks\nStrong proficiency in Python for backend development, data processing, and AI/ML integration\nExperience with FastAPI (mandatory), Flask, or Django for API development\nBasic to intermediate understanding of Node.js for backend integration and collaboration\nBasic understanding of Flutter to support frontend integration of AI APIs\nFamiliarity with cURL for testing, debugging, and managing API requests and responses\n\nMachine Learning & Deep Learning\nSolid understanding of machine learning and deep learning concepts\nHands-on experience with frameworks such as TensorFlow, PyTorch, Keras, or scikit-learn\nExperience in NLP, neural networks, and modern AI architectures\nAbility to train, validate, optimize, and deploy ML/DL models\n\nData & Database Technologies\nExperience with relational databases such as PostgreSQL or MySQL\nExperience with NoSQL and vector databases such as MongoDB, Pinecone, or OpenSearch\nKnowledge of data processing tools such as Pandas and NumPy\nFamiliarity with big data tools such as Spark or Hadoop (optional)\n\nCloud & DevOps\nExperience working with AWS cloud services including Lambda, S3, API Gateway, EC2, ECS/EKS, DynamoDB, and RDS\nKnowledge of Amazon Bedrock and SageMaker is preferred\nExperience with Docker and Kubernetes for containerization and orchestration\nFamiliarity with CI/CD pipelines and DevOps practices\nUnderstanding of IAM, VPC, encryption, and secure system design\n\nProfessional and Technical Skills\nStrong understanding of microservices architecture and distributed systems\nExpertise in API design, software architecture, and scalable system design\nStrong problem-solving, analytical thinking, and debugging skills\nAbility to design, build, test, deploy, and operate AI-powered systems end-to-end\nExperience in performance optimization, scalability, latency, and cost trade-offs\nGood communication skills with the ability to explain complex technical concepts to cross-functional teams\nAbility to assess existing processes, identify improvement areas, and suggest AI-driven solutions\nAwareness of latest technologies and industry trends\n\nGood to Have\nExperience with advanced Agentic AI systems and workflow automation\nKnowledge of Graph RAG and knowledge graph-based retrieval systems\nExperience in prompt optimization, LLM fine-tuning, and model evaluation\nExperience deploying AI/ML/GenAI solutions into production environments\nExposure to multiple cloud platforms such as AWS, Azure, or GCP\nFamiliarity with financial or enterprise domain systems\nExperience with distributed systems, Snowflake, or large-scale data platforms\n\nSummary\nThis role requires a strong foundation in Python backend development combined with hands-on experience in Generative AI, Machine Learning, and Deep Learning.\nThe candidate should be capable of building scalable, production-ready AI systems, integrating advanced models, and enabling intelligent automation across enterprise workflows.