Machine Learning Engineer

Role Overview:
As a Machine Learning Engineer with 3 to 5 years of experience, you will play a pivotal role in developing and implementing AI/ML solutions within our organization. Your expertise will contribute to the successful deployment of AI models, enhancing business outcomes and driving innovation in the AI/ML domain.
Key Responsibilities:
• Design and implement AI/ML reference architecture assets and solutions.
• Deploy and manage AI/ML tools, platforms, and infrastructure effectively.
• Implement ethical AI practices and governance standards.
• Monitor and evaluate the performance of AI/ML initiatives to showcase ROI.
• Develop, train, and deploy AI/ML models and solutions.
• Collaborate with client teams, participate in RFPs, and propose tailored AI/ML solutions.
• Contribute to the development of re-usable methodologies, pipelines, and models.
• Work across various deployment environments and containerization techniques.
• Utilize coding knowledge in languages like R, Python, Scala, MATLAB, etc.
• Apply expertise in solving problems related to Generative AI, Computer Vision, NLP, Predictive Analytics, etc.

Required Skills & Qualifications:
• 3-5 years of experience in AI/ML, preferably in developing and deploying AI models.
• Proficiency in languages such as Python, R, Scala, MATLAB.
• Experience with Enterprise Chatbots, LLMs, RAG, Agentic AI, and vector databases.
• Strong problem-solving skills in areas like Generative AI, Computer Vision, NLP, etc.
• Experience in working with MLOps methods and ML pipelines.
• Ability to work collaboratively in cross-functional teams.
• Strong communication and presentation skills to guide and inspire.
• Bachelor’s or Master’s degree in Computer Science, Data Science, or related field.
Preferred Qualifications:
• Experience with Generative AI applications like Chatbots, AI Agents, Content Creation.
• Familiarity with cloud, on-premises, and hybrid deployment environments.
• Knowledge of containerization techniques like Docker, Kubernetes.
• Exposure to presales activities, business development, and AI/ML project delivery.

Mandatory skills:

AI/ML

Do

• Manage the product/ solution development using the desired AI techniques
  • Lead development and implementation of custom solutions through thoughtful use of modern AI technology
  • Review and evaluate the use cases and decide whether a product can be developed to add business value
  • Create the overall product development strategy and integrating with the larger interfaces
  • Create AI models and framework and implement them to cater to a business problem
  • Draft the desired user Interface and create AI models as per business problem
  • Analyze technology environment and client requirements to define product solutions using AI framework/ architecture
  • Implement the necessary security features as per product’s requirements
  • Review the used case and see the latest AI that can be used in product’s development
  • Identify problem areas and perform root cause analysis and provide relevant solutions to the problem
  • Tracks industry and application trends and relates these to planning current and future AI needs
  • Create and delegate work plans to the programming team for product development
  • Interact with Holmes advisory board for knowledge sharing and best practices
• Responsible for developing and maintaining client relationships with the key strategic partners and decision makers
  • Drive discussions and provide consultation around product design as per customer needs
  • Participate in client interactions and gather insights regarding product development
  • Interact with vertical delivery and business teams and provide and correct responses to RFP/ client requirements
  • Assist in product’s demonstration and receive feedback from the client
  • Design presentations for seminars, meetings and enclave primarily focused over product
• Team Management
  • Resourcing
    • Forecast talent requirements as per the current and future business needs
    • Hire adequate and right resources for the team
  • Talent Management
    • Ensure adequate onboarding and training for the team members to enhance capability & effectiveness
    • Build an internal talent pool and ensure their career progression within the organization
    • Manage team attrition
    • Drive diversity in leadership positions
  • Performance Management
    • Set goals for the team, conduct timely performance reviews and provide constructive feedback to own direct reports
    • Ensure that the Performance Nxt is followed for the entire team
  • Employee Satisfaction and Engagement
    • Lead and drive engagement initiatives for the team
    • Track team satisfaction scores and identify initiatives to build engagement within the team

Deliver