About the Role
We 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.
The 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.
You will be responsible for building high-performance backend services, integrating advanced AI/ML models, and enabling scalable API-driven platforms.
The ideal candidate should have experience in building LLM-powered systems, implementing Agentic AI workflows,
and applying AI-first approaches to solve business problems.
You will work closely with cross-functional teams to deliver reliable, scalable, and secure AI solutions integrated into enterprise systems.
Key Responsibilities
- Design, develop, and integrate LLM-based solutions (e.g., OpenAI GPT, LLaMA, HuggingFace models) into enterprise products and workflows
- Implement Retrieval-Augmented Generation (RAG), prompt engineering, embeddings, chunking strategies, and fine-tuning for business use cases
- Develop APIs and integration layers to seamlessly connect AI models with frontend and backend systems
- Build and maintain scalable backend applications using Python with microservices architecture
- Design and implement RESTful APIs using frameworks such as FastAPI (mandatory), Flask, or Django
- Develop Agentic AI workflows including multi-agent coordination, tool/function calling, memory handling, and workflow orchestration
- Integrate AI models into applications using APIs and ensure secure and efficient communication across systems
- Collaborate effectively with frontend (Flutter) and backend (Node.js/Python) teams for smooth AI feature deployment
- Test, debug, and manage API integrations using tools like cURL and other debugging mechanisms
- Build and deploy AI services on cloud platforms using AWS services such as Lambda, S3, API Gateway, EC2, ECS/EKS, DynamoDB, and RDS
- Leverage Amazon Bedrock and SageMaker for model deployment, orchestration, and scaling
- Develop and integrate machine learning and deep learning models using frameworks such as TensorFlow, PyTorch, and scikit-learn
- Work on NLP, classification, regression, clustering, anomaly detection, and time-series modeling problems
- Build scalable data pipelines for data processing, training, validation, and inference
- Ensure systems are secure, scalable, cost-optimized, and production-ready with proper monitoring and observability
- Implement DevOps and MLOps best practices including CI/CD, model versioning, logging, and performance tracking
- Collaborate with product teams and stakeholders to translate business requirements into AI-driven solutions
- Contribute to architecture design, innovation, and continuous improvement of AI platforms
Required Technical Skills:
LLM & AI Integration (Mandatory – Hands-on)
- Strong hands-on experience working with LLMs and Generative AI systems
- Experience integrating LLMs such as OpenAI GPT, LLaMA, HuggingFace models into real-world applications
- Experience with frameworks such as LangChain, LlamaIndex, LangGraph, ADK, or similar
- Hands-on experience with vector databases such as Pinecone, Weaviate, Milvus, FAISS, or OpenSearch
- Proven ability to build and deploy RAG pipelines, embeddings-based retrieval systems, and prompt engineering workflows
- Experience integrating AI models via APIs into live production systems
Programming & Frameworks
- Strong proficiency in Python for backend development, data processing, and AI/ML integration
- Experience with FastAPI (mandatory), Flask, or Django for API development
- Basic to intermediate understanding of Node.js for backend integration and collaboration
- Basic understanding of Flutter to support frontend integration of AI APIs
- Familiarity with cURL for testing, debugging, and managing API requests and responses
Machine Learning & Deep Learning
- Solid understanding of machine learning and deep learning concepts
- Hands-on experience with frameworks such as TensorFlow, PyTorch, Keras, or scikit-learn
- Experience in NLP, neural networks, and modern AI architectures
- Ability to train, validate, optimize, and deploy ML/DL models
Data & Database Technologies
- Experience with relational databases such as PostgreSQL or MySQL
- Experience with NoSQL and vector databases such as MongoDB, Pinecone, or OpenSearch
- Knowledge of data processing tools such as Pandas and NumPy
- Familiarity with big data tools such as Spark or Hadoop (optional)
Cloud & DevOps
- Experience working with AWS cloud services including Lambda, S3, API Gateway, EC2, ECS/EKS, DynamoDB, and RDS
- Knowledge of Amazon Bedrock and SageMaker is preferred
- Experience with Docker and Kubernetes for containerization and orchestration
- Familiarity with CI/CD pipelines and DevOps practices
- Understanding of IAM, VPC, encryption, and secure system design
Professional and Technical Skills
- Strong understanding of microservices architecture and distributed systems
- Expertise in API design, software architecture, and scalable system design
- Strong problem-solving, analytical thinking, and debugging skills
- Ability to design, build, test, deploy, and operate AI-powered systems end-to-end
- Experience in performance optimization, scalability, latency, and cost trade-offs
- Good communication skills with the ability to explain complex technical concepts to cross-functional teams
- Ability to assess existing processes, identify improvement areas, and suggest AI-driven solutions
- Awareness of latest technologies and industry trends
Good to Have
- Experience with advanced Agentic AI systems and workflow automation
- Knowledge of Graph RAG and knowledge graph-based retrieval systems
- Experience in prompt optimization, LLM fine-tuning, and model evaluation
- Experience deploying AI/ML/GenAI solutions into production environments
- Exposure to multiple cloud platforms such as AWS, Azure, or GCP
- Familiarity with financial or enterprise domain systems
- Experience with distributed systems, Snowflake, or large-scale data platforms
Summary
This role requires a strong foundation in Python backend development combined with hands-on experience in Generative AI, Machine Learning, and Deep Learning.
The candidate should be capable of building scalable, production-ready AI systems, integrating advanced models, and enabling intelligent automation across enterprise workflows.
