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Senior / Lead/Senior Lead GenAI Engineer - Data and AI

Pune
Job Description
Senior / Lead/Senior Lead GenAI Engineer - Data and AI 
Pune
Who   we are
Tiger Analytics is a global leader in AI and analytics, helping   Fortune 1000 companies solve their toughest challenges. We offer full-stack   AI and analytics services & solutions to empower businesses to achieve   real outcomes and value at scale. We are on a mission to push the boundaries   of what AI and analytics can do to help enterprises navigate uncertainty and   move forward decisively. Our purpose is to provide certainty to shape a   better tomorrow.
Our team of 4000+ technologists and consultants are based in the US,   Canada, the UK, India, Singapore and Australia, working closely with clients   across CPG, Retail, Insurance, BFS, Manufacturing, Life Sciences, and   Healthcare. Many of our team leaders rank in Top 10 and 40 Under 40 lists,   exemplifying our dedication to innovation and excellence.
We are a Great Place to Work-Certified™ (2022-25), recognized by   analyst firms such as Forrester, Gartner, HFS, Everest, ISG and others. We   have been ranked among the ‘Best’ and ‘Fastest Growing’ analytics firms lists   by Inc., Financial Times, Economic Times and Analytics India Magazine.
Curious about the role? What your typical day would look like?
The AI Developer is responsible for designing, developing, and deploying artificial intelligence and machine learning solutions that drive business value across the enterprise. This role combines deep technical expertise in AI/ML frameworks, data engineering, and software development to build production-grade intelligent systems — including predictive models, natural language processing applications, computer vision solutions, and generative AI capabilities. The AI Developer collaborates closely with data scientists, software engineers, product owners, and business stakeholders to translate complex business requirements into scalable, reliable AI-powered applications. The ideal candidate is a hands-on engineer who thrives at the intersection of research and production, bringing innovative AI concepts from prototype to enterprise deployment.
Key Responsibilities / Essential Duties
  • Design and develop machine learning models, deep learning architectures, and AI solutions to address business challenges across the organization.
  • Build end-to-end ML pipelines — including data ingestion, feature engineering, model training, evaluation, and deployment — using industry-standard tools and cloud platforms.
  • Develop and integrate RESTful APIs and microservices to serve AI/ML models in production environments with high availability and low latency.
  • Implement generative AI solutions leveraging large language models (LLMs), retrieval-augmented generation (RAG), prompt engineering, and fine-tuning techniques.
  • Collaborate with data scientists, data engineers, and software development teams to translate research prototypes into scalable, production-ready applications.
  • Optimize model performance through hyperparameter tuning, feature selection, A/B testing, and continuous monitoring of model drift and accuracy.
  • Architect scalable AI infrastructure using cloud-native services (Azure AI, AWS SageMaker, or GCP Vertex AI) and containerization technologies (Docker, Kubernetes).
  • Maintain robust MLOps practices including version control for models and data, automated retraining pipelines, CI/CD for ML workflows, and model governance.
  • Ensure responsible AI practices by implementing fairness, explainability, bias detection, and compliance measures aligned with organizational policies and regulatory requirements.
  • Evaluate emerging AI technologies, frameworks, and research papers to identify opportunities for innovation and competitive advantage.
  • Document technical designs, model architectures, experiment results, and deployment procedures to ensure knowledge sharing and reproducibility.
  • Mentor junior developers and cross-functional team members on AI/ML best practices, coding standards, and emerging technologies.
Qualifications
Education
  • Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, Mathematics, or a related technical field (required).
  • Master’s degree or Ph.D. in Artificial Intelligence, Machine Learning, Computer Science, or a related discipline (preferred).
Experience:
  • Minimum professional experience as per the Level in software development with a focus on AI/ML application development.
  • Minimum of hands-on experience as per the Level building, training, and deploying machine learning models in production environments.
  • Demonstrated experience with end-to-end ML pipelines and MLOps practices in a cloud-based environment.
  • Experience in a regulated industry (pharmaceutical, healthcare, or life sciences) is a plus.
  • Certifications (Required / Preferred)
  • Microsoft Certified: Azure AI Engineer Associate or AWS Certified Machine Learning — Specialty — Required (one of the two).
  • Google Professional Machine Learning Engineer — Preferred.
  • NVIDIA Deep Learning Institute (DLI) Certification — Preferred.
  • Stanford / DeepLearning.AI Professional Certificate in Machine Learning or Deep Learning — Preferred.
Knowledge, Skills & Abilities:
  • Expert-level proficiency in Python and its AI/ML ecosystem including TensorFlow, PyTorch, scikit-learn, Hugging Face Transformers, and LangChain.
  • Strong experience with cloud AI/ML services such as Azure Machine Learning, Azure OpenAI Service, AWS SageMaker, or GCP Vertex AI.
  • Proficiency in data manipulation and analysis using Pandas, NumPy, Spark, and SQL across structured and unstructured datasets.
  • Hands-on experience with MLOps tools and practices including MLflow, Kubeflow, Azure ML Pipelines, and automated model retraining workflows.
  • Solid understanding of deep learning architectures including CNNs, RNNs, Transformers, GANs, and diffusion models.
  • Experience with generative AI technologies including large language models (GPT, Claude, LLaMA), vector databases (Pinecone, Weaviate, FAISS), and RAG frameworks.
  • Proficiency in containerization and orchestration technologies (Docker, Kubernetes) and CI/CD pipelines (Azure DevOps, GitHub Actions).
  • Strong software engineering fundamentals including object-oriented design, design patterns, version control (Git), and code review best practices.
  • Excellent communication skills with the ability to explain complex AI concepts to non-technical stakeholders and translate business requirements into technical solutions.
  • Strong analytical and problem-solving abilities with a research-oriented mindset and commitment to staying current with rapidly evolving AI advancements.
You are important to us, let’s stay connected!
Every individual comes with a  different set of skills and qualities so even if you don’t tick all the boxes for the role today, we urge you to apply as there might be a suitable/unique role for you tomorrow. We are an equal-opportunity employer. Our diverse and inclusive culture and values guide us to listen, trust, respect, and encourage people to grow the way they desire.

Note: The designation will be commensurate with expertise and experience.