Senior AI Engineer
Takeda · IND - Bengaluru ·
- Work mode
- Hybrid
- Seniority
- Senior
- Employment
- Full time
- Category
- AI engineering
- Experience
- 6+ years
Takeda · IND - Bengaluru ·
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The Future Begins Here
At Takeda, we are leading digital evolution and global transformation. By building innovative solutions and future-ready capabilities, we are meeting the need of patients, our people, and the planet.
Bengaluru, the city, which is India’s epicenter of Innovation, has been selected to be home to Takeda’s recently launched Innovation Capability Center. We invite you to join our digital transformation journey. In this role, you will have the opportunity to boost your skills and become the heart of an innovative engine that is contributing to global impact and improvement.
At Takeda’s ICC we Unite in Diversity
Takeda is committed to creating an inclusive and collaborative workplace, where individuals are recognized for their backgrounds and abilities they bring to our company. We are continuously improving our collaborators journey in Takeda, and we welcome applications from all qualified candidates. Here, you will feel welcomed, respected, and valued as an important contributor to our diverse team
About the role:
As a Senior AI Engineer, you will provide deep hands-on technical ownership in designing, building, and operationalizing scalable enterprise-grade Generative AI and Agentic AI solutions that deliver trusted, actionable intelligence across the business. You will help establish strong engineering standards for AI development, including structured experimentation, evaluation rigor, secure deployment, and reliable production operations, ensuring AI capabilities are delivered responsibly and at enterprise quality.
You will lead the end-to-end lifecycle of AI implementation from problem framing and proof-of-concept through production deployment, monitoring, and continuous optimization embedding governance, observability, performance efficiency, and cost control into solution design. You will lead the end-to-end lifecycle of AI solutions from problem framing and proof-of-concept through production deployment, monitoring, and continuous optimization embedding governance, observability, performance efficiency, and cost control into solution design & implementation. This is a hands-on builder role requiring true ownership of AI-powered solutions across the product lifecycle. The ideal candidate is well-rounded and willing to contribute beyond model development, including data engineering, backend services, application logic, and user-facing components where needed to ensure reliable, scalable production outcomes.
You will collaborate closely with AI product lead, Solution & Design Leads, Delivery Leads, platform teams, and global PDT DD&T stakeholders across India, Europe, and the United States to accelerate modern AI adoption and enterprise-wide value realization.
This role reports to Delivery lead and is aligned with the Data & Analytics chapter within ICC.
How will you contribute
Gen-AI Solutioning & Delivery
Design and implement end-to-end LLM-powered Generative AI and Agentic AI solutions, including RAG pipelines, agentic workflows, and multi-step reasoning systems.
Translate business problems into scalable AI architectures spanning data, retrieval, model orchestration, and application layers. Ensure clear alignment on success metrics & business value with Product leads
Hands-on leadership & contribution for end-to-end AI solution development spanning data pipelines, service-layer APIs, application integration, and UI enablement, in addition to model, prompt, and framework engineering, to deliver scalable production systems.
Define and maintain reference architectures for GenAI components—RAG pipelines, agentic systems, vector storage, and scalable inference endpoints
Review solution designs and technical approaches created by junior engineers, ensuring alignment to architectural best practices and enterprise engineering standards. Includes evaluating use of agent frameworks (LangGraph, CrewAI, Swarm), validating tooling abstractions, memory strategies, and multiagent collaboration patterns.
Deliver in a globally distributed ecosystem, need to collaborate across Business users, Product lead, Solution & Design leads, Enterprise Architecture teams, Platform teams & other engineering, dev teams
Retrieval, Context Engineering & Agentic Systems
Build and optimize retrieval pipelines using vector databases, hybrid search, reranking, grounding, and provenance tracking.
Develop agent orchestration patterns, tool integrations, and context-management strategies for long-running or multi-agent workflows.
Ensure freshness, accuracy, and traceability of knowledge used in AI-generated outputs.
Model Development, Tuning & Optimization
Evaluate and select foundation models across cloud and self-hosted environments.
Implement fine-tuning, parameter-efficient adaptation, and prompt optimization strategies.
Improve latency, scalability, and cost efficiency using batching, caching, distillation, or quantization techniques.
Infrastructure, Deployment & Observability
Collaborate with Infrastructure and Platform teams to enable scalable, secure, and cost-efficient cloud infrastructure supporting Generative AI and agentic workloads across development, testing, and production environments.
Establish automated deployment strategies for Gen-AI services.
Implement monitoring for quality, hallucinations, drift, latency, and system reliability. Define rollback, governance, and lifecycle management processes for AI and agentic systems
Ensure strong GenAI observability across all solutions, including prompt tracing, metrics instrumentation, log reduction, quality monitoring, and operational telemetry.
Engineering ownership & excellence
Design, build, and maintain scalable data pipelines, backend services, and APIs that power AI-driven applications and production workflows.
Contribute to frontend or user experience components where needed to deliver intuitive, reliable, and production-ready AI solutions.
Apply strong software engineering best practices across the product implementation lifecycle, including version control, CI/CD automation, testing strategies, code quality, and comprehensive documentation
Demonstrate flexibility to contribute beyond core GenAI development, supporting adjacent engineering domains to ensure true end-to-end solution delivery and operational success.
Responsible AI, Security & Compliance
Adhere to privacy, security, and ethical AI standards across data, prompts, models, and outputs.
Implement guardrails, content filtering, access control, and human-in-the-loop review where required.
AI solutions aligned with regulatory expectations and enterprise governance frameworks.
HIPAA/GDPR audits; model cards documenting bias testing (demographic parity <0.8), lineage tracing.
PII anonymization, access controls (RBAC/IAM) for datasets/models.
Protect against adversarial attacks (prompt injection, evasion); robustness testing (Adversarial Robustness Toolbox)
Innovation & Enablement
Continuously evaluate emerging Gen-AI technologies and guide adoption strategy.
Act as a strong advocate for responsible and high-impact AI adoption across the team
Educate stakeholders on AI capabilities, limitations, and best practices, enabling informed and ethical usage.
Drive reusable AI patterns, playbooks, and internal enablement programs to accelerate scalability & maturity.
Mentor peers, fostering a culture of experimentation, learning, and engineering excellence in AI.
Contribute to shaping AI long-term capability roadmap.
Requirements:
Bachelor’s degree in computer science, Engineering, Data Science, or related field.
6+ years of relevant experience in Data Science, Machine Learning, AI engineering.
Hands-on experience building and deploying Generative AI or Agentic AI solutions in enterprise environments, from POC through production.
Proven hands-on experience building and deploying AI/ML solutions using Databricks and AWS, with CI/CD automation through GitHub Actions for reliable, production-grade releases.
Strong proficiency in Python, modern ML frameworks such as PyTorch or TensorFlow, modern AI orchestration frameworks such as Langchain, Langflow
Demonstrated experience delivering production-grade AI-powered applications end-to-end, contributions in aspects including data engineering, backend services, and application or UI components.
Deep understanding of NLP, transformers, embeddings, vector databases, and RAG architecture. Experience deploying scalable AI services on cloud infrastructure. Strong knowledge of monitoring, model lifecycle management, and evaluation frameworks.
Understanding of Responsible AI, privacy, security, and governance in regulated environments.
Demonstrated ability to manage AI solutions for scalability, reliability, and operational cost efficiency.
Excellent problem-solving, communication, and cross-functional collaboration skills in global teams.
Preferred Skills:
Relevant AI focused professional degree, certifications
Experience designing and implementing multi-agent AI systems
Experience defining AI Total Cost of Ownership (TCO), ROI measurement, and enterprise adoption strategy.
Proven track record of mentoring teams and championing modern AI engineering practices.
Background in healthcare, life sciences, or other regulated industries.
Experience in MCP, Agent2Agent communication protocols
BENEFITS:
It is our priority to provide competitive compensation and a benefit package that bridges your personal life with your professional career. Amongst our benefits are:
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