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

Assistant Manager | Machine learning, Artificial Intelligence | Hyderabad | TTC - DOMESTIC
• Job requisition ID : 108111 
• Location: Bengaluru
• Entity: Deloitte Touche Tohmatsu India LLP 

The team

Tax Technology Consulting (TTC) utilizes tax technology and strategy consulting experience to help tax departments pursue a streamlined, transparent, and efficient tax function that enhances the core responsibilities of compliance, reporting, and planning, while also positioning tax as a strategic business advisor for the digital era. Includes support for tax risk management, tax strategy, tax operations, shared services, data and information management, data analytics, technology and systems integration, and process improvement, all of which are related to the tax function. Learn more about our Tax Practice.

Your work profile

• Possess a strong background in artificial intelligence and machine learning, with handson experience in frameworks such as TensorFlow, PyTorch, scikitlearn, 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, AIdriven mathematical insights into compelling narratives and storyboards for business stakeholders.
• Collaborate with crossfunctional 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 highcomplexity 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 finetune 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 datadriven business decisions.
• Stay updated with emerging AI/ML research, tools, and technologies and contribute to continuous improvement of models and systems.
• Design endtoend 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 proofofconcepts (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 rootcause 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 realtime 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 highrisk 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.