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Applications open Full-Time Job

Senior Gen AI Engineer

Orion Innovation

  • Chennai, India
  • Competitive Package
  • 5+ Years
  • Posted Today
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About the role

Orion Innovation is a premier, award-winning, global business and technology services firm. Orion delivers game-changing business transformation and product development rooted in digital strategy, experience design, and engineering, with a unique combination of agility, scale, and maturity. We work with a wide range of clients across many industries including financial services, professional services, telecommunications and media, consumer products, automotive, industrial automation, professional sports and entertainment, life sciences, ecommerce, and education.

Job Overview

We are looking for a Senior Gen AI Engineer with proven expertise in building robust, scalable, and secure backend systems for AI, Generative AI (GenAI), and Data Science applications. The ideal candidate will combine strong backend engineering fundamentals with hands-on experience integrating AI/ML models, optimizing data pipelines, and enabling enterprise-grade deployments. This role is highly collaborative, requiring close work with data scientists, AI architects, and product teams to deliver high-performance solutions.

Key Responsibilities

  • Design, develop, and maintain scalable backend services and APIs in Python (FastAPI, Flask, Django).
  • Build data ingestion, transformation, and feature engineering pipelines for AI/ML and GenAI.
  • Implement microservices with reliability, observability, and maintainability.
  • Optimize backend systems for performance, cost, and scalability in cloud environments.
  • Work with data scientists to productionize ML/GenAI models (deployment, monitoring, lifecycle).
  • Integrate LLMs, vector databases, embeddings, and RAG pipelines into services.
  • Develop APIs and SDKs to expose AI/ML capabilities.
  • Apply responsible AI practices: bias detection, security, compliance.
  • Translate business needs into technical designs with architects and product teams.
  • Mentor junior engineers, enforce best practices, and support CI/CD automation.
  • Contribute to design discussions, architecture reviews, and sprint planning.

Key Skills

  • 6+ years of backend engineering experience with Python in enterprise or product environments.
  • Strong expertise in FastAPI, Flask, Django, or similar frameworks.
  • Proven experience working on AI/ML/GenAI projects (model deployment, serving, integration).
  • Hands-on with data processing frameworks (Pandas, PySpark, Dask) and ML frameworks (PyTorch, TensorFlow, Hugging Face).
  • Familiarity with vector databases (Pinecone, Weaviate, FAISS, Milvus) and orchestration tools (Airflow, Dagster, Prefect).
  • Strong understanding of REST/gRPC APIs, microservices, containerization (Docker, Kubernetes), and CI/CD pipelines.
  • Cloud experience with AWS, Azure, or GCP (serverless, Kubernetes, AI/ML services).
  • Solid understanding of security, scalability, and performance tuning in backend systems.

Preferred Qualifications

  • Experience with LLMOps/ MLOps (MLflow, Weights & Biases, LangChain/LangGraph).
  • Exposure to multi-agent systems, advanced RAG, or AI governance patterns.
  • Prior experience in data-intensive applications (streaming, big data pipelines).
  • Open-source contributions or publications in AI/ML/GenAI.

Orion is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, creed, religion, sex, sexual orientation, gender identity or expression, pregnancy, age, national origin, citizenship status, disability status, genetic information, protected veteran status, or any other characteristic protected by law.

Candidate Privacy Policy

Orion Systems Integrators, LLC And Its Subsidiaries And Its Affiliates (collectively, “Orion,” “we” Or “us”) Are Committed To Protecting Your Privacy. This Candidate Privacy Policy (orioninc.com) (“Notice”) Explains

  • What information we collect during our application and recruitment process and why we collect it;
  • How we handle that information; and
  • How to access and update that information.

Your use of Orion services is governed by any applicable terms in this notice and our general Privacy Policy.

Eligibility & process

Who can apply

Seniority: Mid-Senior level. Employment type: Full-time

Skills & stack

Python Django FastAPI Flask REST APIs Microservices gRPC AWS

Selection process

Details are shared by the recruiter after shortlisting.

Prep kit

19 resources
Study material 4
Company-specific guides 4
Likely coding questions 4
Round-wise strategy 4
Round 1: Online Assessment (OA) Aptitude, Quantitative Logic & 2 Coding Problems Focus on accuracy and speed. Practice arrays, strings, and standard arithmetic puzzles.
Round 2: Technical Interview 1 Core Tech Stack (Python, Django, FastAPI, Flask, REST APIs, Microservices, gRPC, AWS) & Live Code Tracing Explain your thought process aloud. Analyze time & space complexities before coding.
Round 3: System Design & Problem Solving Database Schemas, APIs & Architecture Basics Clarify edge cases, diagram schemas cleanly, and discuss scalability trade-offs.
Round 4: HR & Cultural Fit Discussion Orion Innovation Core Values, Learning Agility & Offer Terms Demonstrate passion, strong communication, and readiness for full-time collaboration.
Technical questions 4
What is the bias-variance tradeoff and how do you prevent overfitting? Answer
Model answer: High bias leads to underfitting (oversimplified model), high variance leads to overfitting (captures noise). Mitigate using L1/L2 Regularization, Dropout, Cross-Validation, and data augmentation.
Explain the difference between Precision, Recall, and F1-Score. Answer
Model answer: Precision = TP / (TP + FP) (correctness of positive predictions). Recall = TP / (TP + FN) (coverage of actual positives). F1-Score is the harmonic mean of Precision and Recall.
How does Gradient Descent work and what is the role of Learning Rate? Answer
Model answer: It optimizes loss functions by iteratively moving weights in the direction of negative gradient. A large learning rate may overshoot the minimum; a small rate causes slow convergence.
What is the difference between Supervised, Unsupervised, and Self-Supervised learning? Answer
Model answer: Supervised uses labeled data (X -> y). Unsupervised finds hidden patterns in unlabeled data (clustering/PCA). Self-supervised generates labels from input data (e.g. masked language modeling in BERT/Transformers).
Behavioural & HR questions 3
Why do you want to join Orion Innovation as a Senior Gen AI Engineer?
Highlight Orion Innovation's market reputation, recent tech innovations, and how your skills in Python directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
How do you handle strict deadlines or sudden scope changes?
Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.
Orion Innovation Competitive Package
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