ArtificiaI Intelligence Lead

•  Minimim 6+ years of experience with designing and implementing AI/ML model, out of which minimum 2+ years on implementing GenAI based solutions.
• Strong implementation expertise around foundation models like computer vision models (ResNet, EfficientNet, YOLO) and Generative Model Transformers (GANs, VAEs) to augment the intelligence of low latency predictive algorithms.
• Experience in development and deployment of Models in battery powered Compute Devices having GPUs/TPUs/NPUs accelerators for offline inferencing.
• Experience with deploying and fine tuning Large Multi-modal models with capability of audio synthesis, generative image and video.
• Conversant with Quantization, Speculative Decoding, Kernel Fusion, CoT, RAG, PeFT.
• Conversant with model evaluation techniques and benchmarks.
• Conversant with design, develop, and optimize data pipelines for ingesting, processing, and transforming large-scale data from various sources (structured and unstructured).
• Create and maintain data models that align with ML requirements.
• Deploy ML models (Computer Vision, NLP) into production systems and operationalize MLOps.
• Knowledge of ML frameworks (TensorFlow, PyTorch, OpenCV) and familiarity with deep learning concepts is a must.
• Experience with cloud platforms (e.g., AWS, GCP, Azure) and containerization (Docker, Kubernetes).
• Strong proficiency in Python, SQL, and ETL tools (e.g., Apache Spark, Airflow).

Mandatory Skill: Device side GenAI implementation. Experience with quantizing inference model (LLM/LMM) and deploying on devices with limited compute and memory. Preferably worked on deploying and running ML/LMM models on NVIDIA, QUALCOMM chipsets, etc.

Good to have skill : Conversant with Quantization, Speculative Decoding, Kernel Fusion.

GGUF, LoRA, PeFT, Knowledge Distillation.

 

 

Role Purpose

The purpose of this role is to develop minimum viable product (MVP) and comprehensive AI solutions that meet and exceed client’s expectations and add value to business.

 

Do

 

1. Manage the product/ solution development using the desired AI techniques
   1. Lead development and implementation of custom solutions through thoughtful use of modern AI technology
   2. Review and evaluate the use cases and decide whether a product can be developed to add business value
   3. Create the overall product development strategy and integrating with the larger interfaces
   4. Create AI models and framework and implement them to cater to a business problem
   5. Draft the desired user Interface and create AI models as per business problem
   6. Analyze technology environment and client requirements to define product solutions using AI framework/ architecture
   7. Implement the necessary security features as per product’s requirements
   8. Review the used case and see the latest AI that can be used in product’s development
   9. Identify problem areas and perform root cause analysis and provide relevant solutions to the problem
   10. Tracks industry and application trends and relates these to planning current and future AI needs
   11. Create and delegate work plans to the programming team for product development
   12. Interact with Holmes advisory board for knowledge sharing and best practices

 

1. Responsible for developing and maintaining client relationships with the key strategic partners and decision makers
   1. Drive discussions and provide consultation around product design as per customer needs
   2. Participate in client interactions and gather insights regarding product development
   3. Interact with vertical delivery and business teams and provide and correct responses to RFP/ client requirements
   4. Assist in product’s demonstration and receive feedback from the client
   5. Design presentations for seminars, meetings and enclave primarily focused over product

 

1. Team Management
   1. Resourcing
      1. Forecast talent requirements as per the current and future business needs
      2. Hire adequate and right resources for the team
   2. Talent Management
      1. Ensure adequate onboarding and training for the team members to enhance capability & effectiveness
      2. Build an internal talent pool and ensure their career progression within the organization

• Manage team attrition

1. Drive diversity in leadership positions

1. Performance Management
   1. Set goals for the team, conduct timely performance reviews and provide constructive feedback to own direct reports
   2. Ensure that the Performance Nxt is followed for the entire team
2. Employee Satisfaction and Engagement
   1. Lead and drive engagement initiatives for the team
   2. Track team satisfaction scores and identify initiatives to build engagement within the team

 

Stakeholder Interaction

 

Stakeholder Type	Stakeholder Identification	Purpose of Interaction
Internal	Pre-sales	For solutioning and product demos
Sales	To understand the business need and client interaction
Holmes Advisory Board	Knowledge sharing and best practices
Internal Functions (HR, Procurement, IRMC, finance)	To find reusable access solution to the problems
Delivery Leadership Teams	Know about RFP and specific problems regarding project
Product manager and theme owner	For product development and strategy
External	Customer	Product demo, deployment etc

 

 

Display

 

Lists the competencies required to perform this role effectively:

• Functional Competencies/ Skill
  • Domain/Industry Knowledge - Awareness and knowledge of broad economic, demographic, technological and global trends within own SL industry - Expert
  • Systems Thinking - Understanding of the Wipro system (interrelatedness, interdependencies and boundaries) and perform problem solving in a complex environment - Master
  • Leveraging Technology - Knowledge of current and upcoming technology (automation, tools and systems) to build efficiencies and effectiveness in own function/ Client organization - Expert
  • Solution Focus - Apply design thinking to bring about new and novel solutions or changes in processes, products or the way business is conducted - Expert
  • Technical Knowledge - Knowledge of AI, ML frameworks to be deployed - Expert

 

Competency Levels
Foundation	Knowledgeable about the competency requirements. Demonstrates (in parts) frequently with minimal support and guidance.
Competent	Consistently demonstrates the full range of the competency without guidance. Extends the competency to difficult and unknown situations as well.
Expert	Applies the competency in all situations and is serves as a guide to others as well.
Master	Coaches others and builds organizational capability in the competency area. Serves as a key resource for that competency and is recognised within the entire organization.

 

• Behavioral Competencies
  • Formulation &Prioritization
  • Client centricity
  • Execution Excellence
  • Passion for Results
  • Confidence
  • Business Acumen

 

 

 

 

Deliver

 

No.	Performance Parameter	Measure
1.	Continuous technical project management & delivery	Adoption of new technologies, IP creation, MVP creation, Number of patents filed, Research papers created
2.	Client Centricity	No. of automation done, On-Time Delivery, cost of delivery, optimal resource allocation
3.	Capability Building & Team Management	% trained on new age skills, Team attrition %, Number of webinars conducted (internal/external)

 

Automotive