Job Title:  Assistant Manager | Machine learning, Artificial Intelligence | Hyderabad | TTC - DOMESTIC

Job Title: Assistant Manager | Tax- Tax Technology Consulting | AIML

Location:  Bangalore/Hyderabad/Kolkata

The team

Innovation, transformation and leadership occur in many ways. At Deloitte, our ability to help solve clients’ most complex issues is distinct. We deliver strategy and implementation, from a business and technology view, to help you lead in the markets where you compete. Learn more about our Tax Practice.

Your work profile

• Possess a strong background in artificial intelligence and machine learning, with hands‑on experience in frameworks such as TensorFlow, PyTorch, scikit‑learn, and other relevant technologies.
• Demonstrate deep understanding of data structures, algorithms, optimization techniques, and distributed computing frameworks.
• Deploy machine learning and deep learning models into production environments, ensuring performance, scalability, and reliability across cloud platforms.
• Work with diverse database systems and apply best practices in version control, containerization, environment management, and MLOps workflows.
• Translate complex, AI‑driven mathematical insights into compelling narratives and storyboards for business stakeholders.
• Collaborate with crossf unctional teams to convert business requirements into working models, algorithms, and technical solutions.
• Execute product roadmaps and contribute to planning and delivery of programs and initiatives defined by product owners.
• Solve complex business problems independently, escalating only high complexity issues when needed.
• Develop and maintain software programs, data processing workflows, algorithms, dashboards, analytical tools, and queries for data cleaning, modelling, integration, and evaluation.
• Build, automate, and optimize data pipelines for data ingestion, validation, mining, modelling, and visualization, especially for large-scale datasets.
• Design, implement, and evaluate advanced machine learning and deep learning algorithms for diverse business applications.
• Modernize and improve legacy models by applying the latest advancements in machine learning, deep learning, and natural language processing.
• Customize and fine‑tune large language models (LLMs) to build generative AI solutions addressing multiple functional and business use cases.
• Apply A/B testing frameworks and experimentation methodologies to assess and improve model quality.
• Take models from research to production using cloud and ML Ops technologies.
• Provide clear, actionable analytical insights to support data‑driven business decisions.
• Stay updated with emerging AI/ML research, tools, and technologies and contribute to continuous improvement of models and systems.
• Design end‑to‑end ML architectures ensuring scalability, robustness, observability, and maintainability.
• Conduct exploratory data analysis (EDA) to identify trends, patterns, anomalies, and correlations influencing model design.
• Develop reusable ML components, templates, APIs, and libraries to accelerate model development and deployment cycles.
• Ensure model transparency, fairness, and compliance through robust interpretability and explainability practices.
• Implement monitoring and alerting systems to track model drift, data issues, performance degradation, and production risks.
• Collaborate with data engineering teams to enhance data governance, data quality, metadata tracking, and lineage documentation.
• Lead proof‑of‑concepts (PoCs) to evaluate the feasibility of new AI/ML techniques and technologies.
• Enhance CI/CD pipelines for ML workflows, including automated training, testing, tracking, and deployment.
• Perform root cause analysis for production issues related to model performance, data integrity, or pipeline reliability.
• Work with business teams to define success metrics, KPIs, and acceptance criteria for ML-driven solutions.
• Mentor junior engineers and contribute to knowledge sharing, code reviews, and best-practice development.
• Optimize models for computation, memory, and latency across batch and real time inference workloads.
• Evaluate new datasets, labeling strategies, synthetic data approaches, and augmentation techniques to improve training efficiency.
• Document model architectures, assumptions, decision logic, datasets, evaluation metrics, and experimental frameworks to ensure reproducibility.
• Support integration of AI components into applications and platforms via APIs, microservices, or embedded inference systems.
• Perform scenario analysis, stress testing, and benchmarking for high risk or largescale model deployments.
• Participate in Agile ceremonies, including sprint planning, backlog refinement, estimation, and retrospective sessions.
• Collaborate with UI/UX teams to ensure AI outputs are presented intuitively across dashboards, interfaces, and products.
• Participate in platform/tool evaluations and provide recommendations for improving the AI/ML technology stack.
• Contribute to the full lifecycle of AI products—from ideation and prototyping to deployment, monitoring, and continuous refinement.
• Maintain experiment tracking, reproducibility, versioning, and audit trails using tools like MLflow, Weights & Biases, or similar platforms.
• Troubleshoot production incidents involving data quality, pipeline failures, integration issues, or unexpected model behaviour.
• Present results, analyses, risks, and recommendations to stakeholders and leadership in a clear, concise manner.

Key skills required: 

• Minimum of 2-5 years of relevant work experience.
• Master's degree in a related field (Statistics, Mathematics or Computer Science) or MBA in Data Science/AI/Analytics
• Experience with database systems such as MySQL, PostgreSQL, or MongoDB.
• Experience in collecting and manipulating structured and unstructured data from multiple data systems (on-premises, cloud-based data sources, APIs, etc)
• Familiarity with version control systems, preferably Git.
• Familiarity with cloud platforms such as AWS, Azure, or Google Cloud.
• Solid understanding of data structures, algorithms, and distributed computing.
• Excellent knowledge of Jupyter Notebooks for experimentation and prototyping.
• Strong programming skills in Python.
• In-depth understanding of machine learning, deep learning & natural language processing (NLP) algorithms.
• Experience with popular machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn.
• Knowledge of containerization tools such as Docker.
• Experience in deploying machine learning models in production environments.
• Excellent problem-solving and communication skills.
• Proficient in using data visualization tools such as Tableau or Matplotlib, or dashboarding packages like Flask, Streamlit.
• Good working knowledge of MS PowerPoint and storyboarding skills to translate mathematical results to business insights.