Back in June, Indian IT services and consulting giant LTIMindtree introduced Canvas.ai, a generative AI platform to accelerate concept-to-value realisation for enterprises, all while adhering to ethical AI principles.
Since then, the Mumbai-based subsidiary of L&T has been making strides in building in-house expertise and infrastructure, allowing their data scientists, AI specialists, and MLOps engineers to engage in R&D projects exploring the potential of LLMs. Moreover, they offer consulting services to guide clients in integrating generative AI into their processes, from use case identification to model deployment.
“Our strategy for generative AI revolves around a combination of in-house expertise development, research and experimentation and client-centric application to deliver AI services that drive value and solve real-world challenges,” said Jitendra Putcha, EVP– Data, Analytics & AI, LTIMindtree told AIM.
With over 20 years in the industry, Putcha is known for solving data challenges globally, promoting next-gen solutions, and leading generative AI initiatives. His career includes leadership roles at Cognizant, focusing on data modernisation, AI, and analytics. AIM spoke to him about LTIMindtree’s AI initiatives, hiring strategy for data science candidates, work culture and more.
The company is actively hiring for different positions in its AI team.
Inside LTIMindtree’s Data Science Lab
The team addresses various core challenges through data science and AI solutions, including enhancing customer experiences by delivering personalised services, optimising operations by automating tasks and improving decision-making, managing risks by identifying anomalies and potential issues, and exploring new opportunities for growth and competitiveness.
One specific example of their work involves assortment planning with APEX Solution in the retail consumer industry, which optimises product placement on retail shelves to maximise sales and enhance related product positioning.
The organisation implements AI and data science by consulting with businesses to identify and prioritise AI use cases, gathering data to achieve actionable insights, and scaling solutions using hyper scaler cloud platforms. They emphasise responsibility by ensuring unbiased AI models.
Strategically, the organisation focuses on various generative AI architectural styles, including using commercial and open-source APIs for tasks like language translation and code conversion, fine-tuning foundation models using their Canvas.ai platform for secure data, employing RAG for real-time data incorporation, and co-investing in building foundation models when access to proprietary data is available through key customer affiliations.
Interview Process
“We are looking for candidates with skills in data science, AI, and ML, with a strong expertise in deep learning and NLP, in-depth domain knowledge, proficiency in Python programming, a learning mindset conducive to prompt engineering work, effective communication skills, and the ability to stay current with the latest trends for adoption,” said Putcha, talking about the interview process at LTIMindtree.
Internally, the company employs an automated screening and assessment platform called WeCP to evaluate candidates before they can apply for AI-related positions.
“To identify exceptional AI talent, especially among the GenAI’s demographic, we engage AI veterans to lead evaluation panels and mentor participants in nationwide hackathons like the Smart India Hackathon and the Singapore-India hackathon,” he added.
The interview process for a lateral talent hunt includes multiple rounds: the screening round, the first technical assessment round, the second comprehensive technical round, and the final round focusing on company culture and values, often involving discussions with the HR team. This well-structured process ensures a holistic assessment of candidates before making a final hiring decision.
However, Putcha elaborated on the most common mistake candidates make when interviewing for a data science role in the company. He said that candidates often neglect to refresh their knowledge of key data science concepts and Python programming skills. Additionally, some candidates struggle to articulate domain-specific business problems and demonstrate how their solutions align with broader outcomes and impacts.
Expectations
When candidates join the data science team at the company, they can expect to serve as strategic advisors to clients, leveraging proprietary platforms and products to drive substantial business improvements such as increased revenue, reduced total cost of ownership, optimised operations, and enhanced fraud and risk detection.
Alongside this, employees will have a plethora of upskilling opportunities that encompass a comprehensive approach to employee growth and AI proficiency. These initiatives include a career framework called ‘My Career My Growth’ for career progression, a focus on skill development with the Shoshin School offering over 5,000 courses and a Hub and Spokes model for AI content incubation.
Work Culture
The company’s work culture is characterised by a strong focus on its people, emphasising employee well-being, empowerment, and societal impact. “This culture is driven by employee-friendly policies, flexible work arrangements, and a performance-driven approach,” Putcha explained.
“What sets us apart from competitors, especially for the data science team, is its unique positioning as both nimble and financially robust. Our solutions are oriented towards benefiting society, making it a compelling and inspiring place to work,” said Putcha. The Yin-Yang model allows for flexibility in work arrangements, with a strong emphasis on continuous learning and innovation.
In terms of diversity, the gender ratio in the AI team is approximately 30%. The company is committed to promoting diversity, equity, and inclusion, creating a safe and inclusive environment for differently-abled employees as part of its DEI Charter.
“Our dynamic learning environment empowers AI specialists to excel in their field, while our unique culture, focus on customer centricity, and innovation-driven approach provide a platform for making a meaningful impact in the industry,” concluded Putcha.
Read more: Data Science Hiring Process at Happiest Minds Tech
