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AI Platform & Architecture Lead

FD India Private Ltd. HQ -
Vadodara, Gujarat

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Job details

Qualifications

BCS
TensorFlow
CI/CD
Azure
Cloud architecture
Law
Computer Science
PyTorch
Spark
Master's degree
Databases
OS Kernels
Cloud security
Bachelor's degree
Machine learning
Continuous integration
APIs
Kafka
Data science
Metadata
Solution architecture
AI
Python
Data Science

Full job description

At Fluor, we are proud to design and build projects and careers. We are committed to fostering a welcoming and collaborative work environment that encourages big-picture thinking, brings out the best in our employees, and helps us develop innovative solutions that contribute to building a better world together. If this sounds like a culture you would like to work in, you’re invited to apply for this role.

Job Description

Role Overview

The AI/ML Architect defines and leads the architecture for modern AI, GenAI, and machine learning solutions that support large, complex EPC/EPCM projects. This role drives scalable AI platform design, LLM augmented analytics, enterprise search modernization, and secure, governed adoption of AI solutions. The architect collaborates across engineering, construction, project controls, supply chain, and corporate digital teams to deliver transformative capabilities. This position requires ~10 years of experience in data engineering and 5 years’ experience driving large AI ML projects and deployments.

Key Responsibilities

Lead end-to-end architecture of AI/ML and GenAI solutions across FEED, EPC and operations-support phases.
Architect enterprise grade Retrieval-Augmented Generation (RAG) systems leveraging vector databases, embedding models, chunking strategies, and semantic pipelines for EPC knowledge search.
Design and implement cognitive search and next generation enterprise search platforms using Azure Cognitive Search, ML embeddings, and multimodal retrieval.
Define and operationalize LLM Ops / MLOps frameworks including CI/CD for models, evaluation pipelines, model monitoring, prompt management, and guardrails.
Implement LLM judges, synthetic evaluation workflows, and automated quality scoring to ensure responsible, reliable AI outputs.
Develop architecture patterns for Azure AI Foundry, Azure ML, Prompt Flow, Azure OpenAI, and cloud native deployment pipelines.
Lead technical design of data ingestion, enrichment, feature stores, and distributed model training (e.g., Spark, Databricks).
Ensure alignment with enterprise data cataloging and governance tools such as Purview and Collibra.
Collaborate with engineering and project execution functions to identify EPC/EPCM specific AI use cases (schedule intelligence, quantity forecasting, construction quality insights, materials optimization, and risk prediction).
Support evaluation and integration of commercial tools, GenAI copilots, and advanced engineering data systems.
Produce architecture documents, reference patterns, solution blueprints, and technical roadmaps for internal and client-facing reviews.
Mentor data scientists, ML engineers, and developers on best practices in AI architecture, cloud engineering, and LLM-driven workflow design.

Basic Job Requirements

Bachelor’s degree in Computer Science, Data Science, Engineering, or related field (Master’s preferred).
Relevant AI/ML, cloud, or architecture certifications preferred.
10+ years of experience in AI/ML, data engineering, or solution architecture roles.
Hands-on experience architecting and deploying AI/ML or GenAI solutions at enterprise scale.
Strong proficiency in Python, ML frameworks (PyTorch, TensorFlow, Scikit‑learn), and distributed compute (Spark).
Expertise with Azure AI ecosystem (Azure ML, Azure AI Foundry, Azure OpenAI, Databricks, ADF, Synapse).
Experience designing RAG architectures, vector stores (Milvus, FAISS, Qdrant, Chroma, Azure AI Search vector index), and embedding model pipelines.
Deep understanding of MLOps / LLM Ops including model registries, monitoring, orchestration, and evaluation.
Strong understanding of cloud security, identity, data governance, and compliance.
Ability to translate complex requirements into long-term scalable AI architectures.
Experience with Azure Cognitive Search (index design, enrichment pipelines, semantic + hybrid retrieval).
Hands-on experience deploying or fine-tuning self‑hosted LLMs in on‑prem or VNet-isolated environments using GPU clusters.
Strong understanding of vector databases (Pinecone, Weaviate, Milvus, Chroma, pgvector).
Expertise in Responsible AI, model governance, jailbreak testing, and prompt-injection defense.
Experience optimizing LLM performance using quantization, ONNX Runtime, TensorRT, DeepSpeed.

Other Job Requirements

Preferred Qualifications

EPC/EPCM project experience in energy, chemicals, mining, infrastructure, ATLS, or data centers.
Experience with engineering data ecosystems (Hexagon SmartPlant, Aveva, BIM/Digital Twin systems).
Knowledge of Azure Prompt Flow, Function Calling, agents/orchestration frameworks, and safety/guardrail patterns.
Familiarity with Purview, Collibra, and metadata-driven governance.
Certifications such as Azure Solutions Architect Expert, Azure Data Engineer, or Databricks Architect.
Experience with agentic frameworks (Semantic Kernel, AutoGen, CrewAI, LangGraph).
Familiarity with LLM observability (App Insights, Prometheus, OpenTelemetry).
Experience with enterprise indexing, metadata extraction, and enrichment platforms.
Knowledge of secure multi-tenant AI architecture for large engineering organizations.
Expertise with API gateways, service mesh, and event-driven architectures (Kafka/Event Hub).

To be Considered Candidates:
Must be authorized to work in the country where the position is located.

We are an equal opportunity employer. All qualified individuals will receive consideration for employment without regard to race, color, age, sex, sexual orientation, gender identity, religion, national origin, disability, veteran status, genetic information, or any other criteria protected by governing law.

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