AI Architect

Description -

This role is responsible for enabling innovation and creativity by bringing cutting edge perspectives on adopting latest data mining and modelling techniques. The role understands current complex business problems and future business strategy to assess, build and deploy required data mining and modelling capabilities. The role is involved in driving standardization, productivity and cross team learning by establishing processes and SOPs for entire data model development lifecycle. The role drives excellence through continuous improvement in model accuracy and reliability.

Responsibilities

Responsible for the architecture and technical leadership of the end-to-end AI ecosystem including multimodal data pipelines, LLM-based engineering copilots, agentic automation systems, and predictive models for product development monitoring.

• Architect the full stack AI solution (LLMs, ML, agents, APIs, pipelines).

• Design multi-agent workflows for automated monitoring, anomaly detection & reporting.

• Establish LLM fine-tuning, evaluation, RAG, Semantic modelling(NLP/AI)and prompt engineering frameworks.

• Integrate engineering domain data (mechanical, electrical, field, test logs).

• Define MLOps, model governance, observability, and monitoring strategies.

• Collaborate with product development & engineering teams to map use cases.

Skill Set Required - AI/ML AI Architecture & LLM

• Large Language Models (fine-tuning, RAG, distillation)

• Agentic AI frameworks (OpenAI agents, LangGraph, AutoGen, CrewAI)

• Multimodal AI (text + time-series + telemetry + image)

• Knowledge Graphs (Neo4j, RDF, GraphRAG)

• End-to-end pipeline design

• Vector DBs (Pinecone, Milvus, FAISS)

• Cloud AI (Azure AI Studio, AWS)

• Microservices & API integration

• Machine Learning and Deep Learning

• ReactJS, Java Script, Streamlit, Flask

MLOps

• MLflow, Kubeflow, Airflow

• Model monitoring, evaluation, CI/CD

Domain

• Understanding product development lifecycle

• Familiarity with mechanical/electrical engineering data formats

Education and Experience Required

• Four-year or Graduate Degree in Mathematics, Statistics, Economics, Computer Science, Data Science, or any other related discipline or commensurate work experience or demonstrated competence.

• 7-10 years of work experience, preferably in data analytics, statistical modeling, machine learning, or a related field.

Preferred Certifications

• Programming Language/s Certification (SQL, Python, or similar)

Additional Knowledge & Skills

• Algorithms

• Amazon Web Services

• Apache Spark

• Artificial Intelligence

• Big Data

• Computer Science

• Data Analysis

• Data Mining

• Data Science

• Data Visualization

• Natural Language Processing

• Predictive Modeling

• Python (Programming Language)

• SQL (Programming Language)

• Statistical Modeling

• Statistics

• Power Platforms - PowerBI, PowerApps

Cross-Org Skills

• Effective Communication

• Results Orientation

• Learning Agility

• Digital Fluency

• Customer Centricity

Impact & Scope

• Impacts function and leads and/or provides expertise to functional project teams and may participate in cross-functional initiatives.

Complexity

• Works on complex problems where analysis of situations or data requires an in-depth evaluation of multiple factors.

Disclaimer
This job description describes the general nature and level of work performed in this role. It is not intended to be an exhaustive list of all duties, skills, responsibilities, knowledge, etc. These may be subject to change and additional functions may be assigned as needed by management.

The pay range for this role is $147,050 to $230,850 USD annually with additional opportunities for pay in the form of bonus and/or equity (applies to United States of America candidates only). Pay varies by work location, job-related knowledge, skills, and experience.

Benefits: