AI Engineer

Job description:

Job Description

Role Purpose

The purpose of the role is to define, architect and lead delivery of machine learning and AI solutions

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Do

1. Demand generation through support in Solution development

a. Support Go-To-Market strategy

i. Collaborate with sales, pre-sales &consulting team to assist in creating solutions and propositions for proactive demand generation

ii. Contribute to development solutions, proof of concepts aligned to key offerings to enable solution led sales

b. Collaborate with different colleges and institutes for recruitment, joint research initiatives and provide data science courses

2. Revenue generation through Building & operationalizing Machine Learning, Deep Learning solutions

a. Develop Machine Learning / Deep learning models for decision augmentation or for automation solutions

b. Collaborate with ML Engineers, Data engineers and IT to evaluate ML deployment options

c. Integrate model performance management tools into the current business infrastructure

3. Team Management

a. Resourcing

i. Support recruitment process to on-board right resources for the team

b. Talent Management

i. Support on boarding and training for the team members to enhance capability & effectiveness

ii. Manage team attrition

c. Performance Management

i. Conduct timely performance reviews and provide constructive feedback to own direct reports

ii. Be a role model to team for five habits

iii. Ensure that the Performance Nxt is followed for the entire team

d. Employee Satisfaction and Engagement

i. Lead and drive engagement initiatives for the team

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Deliver

No. Performance Parameter Measure  1. Demand generation Order booking 2. Revenue generation through delivery Timeliness, customer success stories, customer use cases 3. Capability Building & Team Management % trained on new skills, Team attrition %͏ ͏

• Design and engineer software with the customer/user experience as a key objective
• Actively contribute to Technology Engineering Practice
• Adhere to all best practice, standards and policies
• Ensure service resilience, service sustainability and recovery time objectives for all the solutions delivered
• Keep up to date and have expertise on current tools, technologies and areas like cyber security and regulations pertaining to aspects like data privacy, consent, data residency etc. that are applicable,  including Responsible AI principles (bias, fairness, transparency, safety/guardrails)
• Ensuring compliance with all relevant controls and standards
• Designing and implementing solutions using the technologies listed
• Delivering high performing solutions to complex problems
• Developing the most appropriate IT solutions in line with the solution design, to meet customer needs
• Ensuring continuous improvement with responsibility to write the unit, integration & automation tests,  and evaluation pipelines for RAG/agentic systems (retrieval quality, groundedness, task success)
• Working closely with Agile leads, Product Owner, Technical Leads, Data analysts, QA engineers, and Business Analysts throughout the project lifecycle
• Performing & Leading Deployments to various environments using DevOps tools/CICD Pipelines,  containerized via Docker
• Technical Leadership skills to guide & mentor junior developers

To be successful in this role, you should meet the following requirements:

• 6-10 years of experience in AI, NLP, ML or software engineering with at least 2-3 years of hands-on work in GenAI/LLM/Prompt Engineering
• Strong Knowledge of LLMs, transformer architecture, and how to interact with models using structured prompting
• Knowledge of different prompting techniques
• Knowledge of NLP techniques like NER, POS, Topic Modelling, KeyPhrase Extraction etc
• Experience with LLM Orchastration frameworks(eg: LangChain/LlamaIndex), RAG and Vector Databases,  including end-to-end RAG design: ingestion/chunking pipelines, hybrid search (dense + sparse/BM25), reranking, latency/cost optimization, and guardrails for hallucination/safety
• Experience with Agentic Frameworks ( e.g., LangGraph, CrewAI, AutoGen, Semantic Kernel ),  covering tool/function calling, ReAct-style reasoning, routing, planning, memory (short/long-term), multi-agent orchestration, and agent evaluation
• Familiarity with Model Context Protocol (MCP) for standardized tool/context integration across agents
• Knowledge…