Role Overview:
Incedo is a US based consulting, analytics and technology services firm incorporated in 2011. We have the Head Quarter in New Jersey with 4000+ employees based globally out of the offices in New Jersey, California, Canada, Mexico, Gurgaon, Pune, Bangalore and Chennai. We are a global digital transformation expert empowering companies to realize sustainable business impact from their digital investments across Banking, Wealth Management, Telecom, Life Science & Healthcare and Hi-Tech sectors. Our integrated services and platforms, from strategy to execution, are built on the foundation of Design, AI, GenAI and Data and strong Engineering capabilities combined with our deep domain expertise.
With over 4,000 professionals and a large, diverse portfolio of long-term, Fortune 500, and fast-growing clients in the US, we not only stive to maximize the ROI from applications of AI @ Scale for our clients, but also do that while striving to become the employer of choice in India and in the US.
Over the years, Incedo has invested in developing multiple innovative Data & AI Platforms and Solutions to help enterprises expedite Al-driven transformative value creation at scale. We are seeking a hands-on Technical Architect with deep expertise in software design, data science, and cloud-native AI/ML deployment. The ideal candidate will drive end-to-end architecture from model design to scalable deployment, ensuring performance, quality, and automation across environments.
Key Responsibilities:
- Architecture & Design
- Design solutions following cloud-native, containerized, and event-driven patterns.
- Define and implement scalable, modular architectures for AI/ML platforms and microservices.
- Apply architectural principles such as separation of concerns, domain-driven design, security and high availability.
- Architecture for scalable generative AI solutions and LLMs
- Hands-On experience with design and deployment of GenAI based solutions.
- Cloud & Infrastructure
- Architect and deploy workloads on Cloud preferably AWS
- Containerize applications using Docker and orchestrate via Kubernetes (K8s).
- Drive the build and maintenance of CI/CD pipelines using GitHub Actions / Jenkins / CodePipeline.
- Drive the Implementation of Quality Gates, static analysis, and automated test triggers in CI/CD flows.
- Software Development
- Lead backend development in Java and Python, adhering to best coding practices and design patterns.
- Strong exposure towards architecting REST APIs.
- Ensure comprehensive unit test coverage and maintain high code quality metrics.
- Mentor teams in code review, refactoring, and design improvement.
- Utilization of modern tools in SDLC for faster development and deployment to production.
- Machine Learning / Data Science
- Architect ML workflows for classification, regression, and anomaly detection use cases.
- Evaluate models using statistical and ML metrics (e.g., precision, recall, ROC-AUC, MSE).
- Implement RAG (Retrieval-Augmented Generation) chatbots and integrate them with enterprise data.
- Drive the development and design of GenAI based solutions.
- Drive end-to-end lifecycle data preprocessing, model training, validation, deployment, and monitoring.
- Integrate ML models into production systems via REST APIs or custom microservices.
- Model Deployment & MLOps
- Build automated model deployment and retraining pipelines.
- Implement monitoring, drift detection, and performance evaluation for deployed models.
- Collaborate with DevOps and Data Engineering teams to standardize MLOps practices.
- Cross-Functional Collaboration
- Partner with product owners, data scientists, and engineers to translate business needs into scalable solutions.
- Define architecture roadmaps, conduct design reviews, and enforce security and compliance standards.
Required Skills & Experience:
- 15+ years in software engineering, including 4+ years in architecture or lead design roles.
- Strong proficiency in Java, Python, and RESTful API design.
- Hands-on experience with Kubernetes, Docker, and one cloud preferably AWS.
- Expertise in CI/CD, GitOps, and enforcing Quality Gates (e.g., SonarQube).
- Solid understanding of ML algorithms (classification, regression, anomaly detection) and model evaluation.
- HandsOn experience in LLM-based Solutions and RAG chatbot architectures.
- Working knowledge of data pipelines, feature stores, and observability tools.
- Excellent communication and stakeholder management skills.
