Python Developer

Key Responsibilities

1. Full Stack Development • Design, develop, and maintain scalable web applications using Python and Django. • Build and maintain REST APIs using Django REST Framework. • Work on backend architecture, business logic, database schema, and API integrations. • Integrate frontend applications with backend APIs and ensure smooth end-to-end functionality. • Write clean, reusable, testable, and maintainable code. • Optimize application performance, database queries, API response time, and server-side logic. • Debug and resolve production and staging issues in a structured manner. • Review code written by team members and enforce coding standards. • Prepare and maintain technical documentation, API documentation, and deployment notes. • Participate in architecture decisions and suggest scalable technical solutions. Senior Full Stack Django Developer cum Project Manager JD | Hinjawadi, Pune

2. Project Management and Delivery • Understand client and internal product requirements and convert them into actionable technical tasks. • Prepare project plans, sprint plans, milestones, and delivery timelines. • Assign tasks to developers, QA, UI/UX, and support team members based on priorities. • Track daily progress, remove blockers, and ensure timely completion of planned work. • Conduct daily stand-ups, sprint reviews, release planning, and internal status meetings. • Coordinate between product, development, QA, implementation, support, and management teams. • Identify delivery risks early and take corrective action before timelines are impacted. • Ensure proper UAT support, bug tracking, release notes, and production deployment coordination. • Maintain task boards and project documentation using Jira, ClickUp, Trello, Asana, or similar tools.

3. Team Leadership • Lead and manage a team of approximately 8 members, including developers, testers, and other technical contributors. • Mentor junior and mid-level developers on technical implementation and best practices. • Ensure task ownership, accountability, and disciplined execution within the team. • Conduct code reviews and guide the team on better architecture, security, and performance practices. • Help team members resolve technical blockers and improve delivery quality. • Support onboarding and training of new developers when required. • Build a positive, structured, and performance-driven engineering culture.

4. Client Communication and Product Demos • Handle client calls, requirement discussions, project update meetings, and product walkthroughs. • Conduct confident and professional client demos for developed modules and product features. • Explain technical and product workflows to clients in simple business language. • Collect client feedback and convert it into clear development tasks. • Coordinate with clients during UAT, go-live, training, and post-deployment support. • Communicate timelines, dependencies, risks, and progress updates clearly. • Build trust with clients by ensuring structured communication and timely delivery.

We are looking for a Technical Lead - GenAI with a strong foundation in Python, Data Analytics, Data Science or Data Engineering, system design, and practical experience in building and deploying Agentic Generative AI systems. The ideal candidate is passionate about solving complex problems using LLMs, understands the architecture of modern AI agent frameworks like LangChain/LangGraph, and can deliver scalable, cloud-native back-end services with a GenAI focus.

Key Responsibilities :

- Design and implement robust, scalable back-end systems for GenAI agent-based platforms.

- Work closely with AI researchers and front-end teams to integrate LLMs and agentic workflows into production services.

- Develop and maintain services using Python (FastAPI/Django/Flask), with best practices in modularity and performance.

- Leverage and extend frameworks like LangChain, LangGraph, and similar to orchestrate tool-augmented AI agents.

- Design and deploy systems in Azure Cloud, including usage of serverless functions, Kubernetes, and scalable data services.

- Build and maintain event-driven / streaming architectures using Kafka, Event Hubs, or other messaging frameworks.

- Implement inter-service communication using gRPC and REST.

- Contribute to architectural discussions, especially around distributed systems, data flow, and fault tolerance.

Required Skills & Qualifications :

- Strong hands-on back-end development experience in Python along with Data Analytics or Data Science.

- Strong track record on platforms like LeetCode or in real-world algorithmic/system problem-solving.

- Deep knowledge of at least one Python web framework (e.g., FastAPI, Flask, Django).

- Solid understanding of LangChain, LangGraph, or equivalent LLM agent orchestration tools.

- 2+ years of hands-on experience in Generative AI systems and LLM-based platforms.

- Proven experience with system architecture, distributed systems, and microservices.

- Strong familiarity with Any Cloud infrastructure and deployment practices.

- Should know about any Data Engineering or Analytics expertise (Preferred) e.g. Azure Data Factory, Snowflake, Databricks, ETL tools Talend, Informatica or Power BI, Tableau, Data modelling, Datawarehouse development.

Experience in developing applications using .Net Core Ver. 6/7, ASP.Net MVC (C#), .Net Core Web API & Web App, Web Services, OOPS, SOLID principles.

Experience in software development and system architecture with Microsoft technologies.

Able to integrate the database objects with the Microservices.

Expert working knowledge of Multi-threading.

Solid experience in converting .NET Framework multi-threaded services into .NET Core.

Worked as part of a cross-functional team on code reviews, internal infrastructure, and process enhancement.

Understanding of CI/CD pipeline is mandatory.

Strong hands-on experience with C# and solid principles.

Expertise in entity framework core with code first approach.

Expertise in writing RESTful services.

Knowledge of RDBMS concepts.

Must have:

• 8+ years of experience with a significant focus on developing, deploying & supporting AI solutions in production environments.
• Proven experience in building enterprise software products for B2B businesses, particularly in the supply chain domain.
• Good understanding of Generics, OOPs concepts & Design Patterns
• Solid engineering and coding skills. Ability to write high-performance production quality code in Python
• Proficiency with ML libraries and frameworks (e.g., Pandas, TensorFlow, PyTorch, scikit-learn).
• Strong expertise in time series forecasting using stat, ML, DL and foundation models
• Experience of working on processing time series data employing techniques such as decomposition, clustering, outlier detection & treatment
• Exposure to generative AI models and agent architectures on platforms such as AWS Bedrock, Crew AI, Mosaic/Databricks, Azure
• Experience of working with modern data architectures, including data lakes and data warehouses, having leveraged one or more of the frameworks such as Airbyte, Airflow, Dagster, AWS Glue, Snowflake,, DBT
• Hands-on experience with cloud platforms (e.g., AWS, Azure, GCP) and deploying ML models in cloud environments.
• Excellent problem-solving skills and the ability to work independently as well as in a collaborative team environment.
• Effective communication skills, with the ability to convey complex technical concepts to non-technical stakeholders

Good To Have:

• Experience with MLOps tools and practices for continuous integration and deployment of ML models.
• Has familiarity with deploying applications on Kubernetes
• Knowledge of supply chain management principles and challenges.
• A Master's or Ph.D. in Computer Science, Machine Learning, Data Science, or a related field is preferred

• Back-end development using Python/Django
• Front-end development using CSS, HTML and JS
• Write reusable, testable, and efficient code
• Implement security and data protection
• Use Amazon Relational Database Service
• Commit, push, pull and sync to Bitbucket, GitLab
• Deployment of code on MS Azure and AWS
• Build efficient scripts and cron jobs in GCP
• Connect apps and automate workflows using Integromat