Job Description

Role Purpose

The purpose of this role is to design, test and maintain software programs for operating systems or applications which needs to be deployed at a client end and ensure its meet 100% quality assurance parameters

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Do

1. Instrumental in understanding the requirements and design of the product/ software

• Develop software solutions by studying information needs, studying systems flow, data usage and work processes
• Investigating problem areas followed by the software development life cycle
• Facilitate root cause analysis of the system issues and problem statement
• Identify ideas to improve system performance and impact availability
• Analyze client requirements and convert requirements to feasible design
• Collaborate with functional teams or systems analysts who carry out the detailed investigation into software requirements
• Conferring with project managers to obtain information on software capabilities

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2. Perform coding and ensure optimal software/ module development

• Determine operational feasibility by evaluating analysis, problem definition, requirements, software development and proposed software
• Develop and automate processes for software validation by setting up and designing test cases/scenarios/usage cases, and executing these cases
• Modifying software to fix errors, adapt it to new hardware, improve its performance, or upgrade interfaces.
• Analyzing information to recommend and plan the installation of new systems or modifications of an existing system
• Ensuring that code is error free or has no bugs and test failure
• Preparing reports on programming project specifications, activities and status
• Ensure all the codes are raised as per the norm defined for project / program / account with clear description and replication patterns
• Compile timely, comprehensive and accurate documentation and reports as requested
• Coordinating with the team on daily project status and progress and documenting it
• Providing feedback on usability and serviceability, trace the result to quality risk and report it to concerned stakeholders

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Key Responsibilities

· Design, train, and deploy machine learning and deep learning models across supervised,

unsupervised, and reinforcement learning domains.

· Implement and optimize algorithms including linear/logistic regression, decision trees,

random forests, SVMs, k-means, PCA, CNNs, RNNs, GANs, and transformers.

· Manage the end-to-end ML lifecycle: data preprocessing, model training, hyperparameter

tuning, evaluation (F1, accuracy, precision/recall), and deployment.

· Experiment with generative AI models, refining hyperparameters and configurations to

improve quality and performance.

· Optimize models for scalability and real-time inference in production environments.

· Collaborate with cross-functional teams to groom datasets, integrate pipelines, and ensure

smooth deployment following Agile practices.

· Debug, validate, and continuously monitor model performance, ensuring improvements

post-delivery.

Job Requirement

· AI/ML Experience: Minimum 3+ years in machine learning, deep learning, and computer

vision projects/products.

· Core ML Knowledge: Strong understanding of supervised, unsupervised, and reinforcement

learning; common algorithms such as linear/logistic regression, decision trees, random

forests, SVMs, k means, PCA, CNNs, RNNs, GANs, and transformers.

· Model Lifecycle: Practical knowledge of training, tuning (hyperparameters), evaluation (F1, accuracy), deployment, and data processing. · Math & Stats: Solid foundation in statistics, linear algebra, and probability. · Programming: Proficient in Python, strong in ML libraries like TensorFlow, PyTorch · Optimize model performance and scalability for real-time inference and deployment. · Experiment with different hyperparameters and model configurations to improve generative AI model quality. · Good to have Generative AI expertise/experience, familiarity with LLMs, RAG pipelines, multimodal models, and frameworks like LangChain/LangGraph, Vector DB (e.g., Pinecone, Weaviate, FAISS) for indexing, embedding, and retrieval. · Software Engineering: Agile practices, debugging. · Systems Knowledge: Understanding of distributed systems and cloud technologies. · Collaboration & Communication: Ability to work across teams and explain technical concepts to varied audiences.

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Deliver