Senior AI Engineer

WSP in India; Bengaluru; On-site; INR 1,800,000 - 3,000,000; Full time

Job summary

WSP India is seeking a Sr AI Engineer to deliver production AI apps on Azure, ensuring accuracy, cost efficiency, latency, safety, drift and uptime. You will deploy, monitor and iterate ML and LLM-powered solutions with strong governance and cross-functional collaboration.

Required: 3–4 years in software/ML engineering with GenAI production, solid ML fundamentals, Python, and Azure ML stack expertise. Hands-on with MLOps, guardrails, and orchestration frameworks.

Qualifications

• Minimum 3–4 years of hands-on software/ML engineering with 2+ years in GenAI/LLM production.
• Solid foundation in AI/ML fundamentals and model evaluation metrics.
• Experience with ML libraries and at least one DL framework (PyTorch or TensorFlow).
• Strong grasp of the Azure AI/ML stack and deployment tooling.
• Hands-on with MLOps tooling: CI/CD, versioning, testing, experiment tracking.
• Proven ability to build guardrails and safety layers for production AI.
• Proficient Python, production-grade code, and scripting.
• Familiarity with orchestration frameworks (LangGraph, Semantic Kernel, LangChain).
• Understanding of RAG architecture and vector store concepts.
• Experience with observability tools for live AI systems.

Responsibilities

• Own end-to-end deployment of AI applications on Azure—from model to production release and monitoring.
• Develop core ML models when GenAI isn’t appropriate—classification, regression, forecasting, etc.
• Design and implement guardrails for safety, filtering, and prompt safety.
• Build and manage MLOps/LLMOps pipelines, versioning, and rollback capabilities.
• Monitor model performance, cost, latency, drift, and uptime; set up observability.
• Architect RAG and agentic systems, including vector stores and retrieval tuning.
• Collaborate with product owners and stakeholders to scope AI features.

Skills

Python programming

ML engineering

Azure ML stack

MLOps

Guardrails & safety

LangGraph/Semantic Kernel

RAG architecture

Monitoring & observability

Tools

PyTorch

TensorFlow

scikit-learn

pandas

NumPy

XGBoost / LightGBM

Docker

Job description

Job Description

We're looking for a Sr AI Engineer who has shipped AI products, not just prototyped them, with equal footing in core ML and modern GenAI. This role sits at the intersection of engineering rigor and product ownership, you'll build, deploy, and operate ML and LLM-powered applications on Azure, with real accountability for what happens after go-live: accuracy, cost, latency, safety, drift, and uptime. If you've only worked in notebooks or built demos that never saw production traffic, this isn't the role. If you've had to explain to a stakeholder why a model started hallucinating in week three or had to design a rollback plan for a prompt change, we want to talk to you.

Responsibilities

What You'll Do

• Own end-to-end deployment of AI applications on Azure — from classical ML models and Azure OpenAI Service integrations through to production release, monitoring, and iteration.
• Build and evaluate core ML models where GenAI isn't the right tool — classification, regression, forecasting, clustering, or recommendation problems using traditional ML techniques.
• Design and implement guardrails — content filtering, prompt injection defence, PII redaction, output validation, and human-in-the-loop checkpoints for high-risk actions.
• Build and maintain MLOps/LLMOps pipelines — CI/CD for models and prompts, feature engineering and data pipelines, automated evaluation harnesses, versioning for models/prompts/embeddings/fine-tunes, and rollback mechanisms.
• Manage the model lifecycle — model selection and routing (classical ML vs. smaller LLMs vs. frontier models by task complexity and cost), performance benchmarking, cost-per-call/cost-per-inference tracking, and deprecation/upgrade planning.
• Implement observability — logging, tracing, and alerting for LLM applications (token usage, latency, hallucination/error rates, user feedback loops).
• Architect RAG and agentic systems — vector store design, retrieval tuning, orchestration frameworks (LangGraph, Semantic Kernel, or equivalent), and multi-agent workflows where applicable.
• Collaborate cross-functionally with product owners, architects, and business stakeholders to translate requirements into scoped, deployable AI features.
• Contribute to AI governance — support responsible AI reviews, model risk assessments, and documentation required for enterprise sign-off.

Qualifications

What You Bring

Must-Have

• Minimum of 3–4 years of hands‑on software/ML engineering experience, with at least 2 years specifically on GenAI/LLM applications taken to production.
• Solid grounding in core AI/ML fundamentals — supervised/unsupervised learning, model evaluation metrics, feature engineering, handling class imbalance/overfitting, and knowing when a classical ML model beats an LLM for the job.
• Hands‑on experience with standard ML libraries (scikit‑learn, XGBoost / LightGBM, pandas, NumPy) and at least one deep learning framework (PyTorch or TensorFlow).
• Strong working knowledge of the Azure AI/ML stack: Azure Machine Learning, Azure OpenAI Service, Azure AI Foundry, Azure AI Search, and Azure App Service/Functions for deployment.
• Practical experience with MLOps/LLMOps tooling — CI/CD pipelines, containerization (Docker), model/prompt versioning, experiment tracking, and automated testing/evaluation frameworks.
• Demonstrated experience building guardrails and safety layers in production — not just theoretical familiarity (e.g., Azure AI Content Safety, custom validation layers, jailbreak/prompt‑injection mitigation).
• Solid Python engineering skills — clean, testable, production‑grade code, not notebook scripts.
• Experience with at least one orchestration framework: LangGraph, Semantic Kernel, LangChain, or similar.
• Understanding of RAG architecture — chunking strategies, embedding models, vector databases, retrieval evaluation.
• Comfort with monitoring/observability tooling (Application Insights, or equivalent) for live AI systems, including model performance monitoring and drift detection.

Good to Have

• Exposure to Copilot Studio or Power Platform for low‑code AI extensions.
• Experience with voice‑based or multimodal AI applications.
• Familiarity with enterprise AI governance frameworks and responsible AI principles.
• Prior experience in AEC, GCC, or large enterprise delivery environments.
• Contributions to internal upskilling, documentation, or mentoring within an AI team.

What Sets Strong Candidates Apart

We're specifically screening for deployment maturity across both classical ML and GenAI, over research depth alone. In interviews, be ready to talk through:

• A time you had to redesign a guardrail after it failed in production.
• How you've tracked and controlled LLM cost at scale (tiered model routing, caching, batching).
• Your approach to versioning and rolling back a prompt or model change without breaking downstream consumers.
• How you've measured and reduced hallucination or drift in a live system.
• A time you chose (or should have chosen) a classical ML model over an LLM, and why.

BGV

• Employment with WSP India is subject to the successful completion of a background verification (“BGV”) check conducted by a third‑party agency appointed by WSP India.
• Candidates are advised to ensure that all information provided during the recruitment process — including documents uploaded — is accurate and complete, both to WSP India and its BGV partner.

About Us

WSP is one of the world's leading professional services consulting firms. We are dedicated to our local communities and propelled by international brainpower. We are technical experts and strategic advisors including engineers, technicians, scientists, architects, planners, surveyors and environmental specialists, as well as other design, program and construction management professionals. We design lasting solutions in the Transportation & Infrastructure, Property & Buildings, Earth & Environment, Power & Energy, Resources and Industry sectors, as well as offering strategic advisory services. Our talented people around the globe engineer projects that will help societies grow for lifetimes to come.

With approximately 4,000 talented people across 3 locations (Noida, Bengaluru & Mumbai offices) in India and more than 73,000 globally, in 550 offices across 40 countries, we engineer projects that will help societies grow for lifetimes to come.

At “WSP” we draw on the diverse skills and capabilities of our employees globally to compete for the most exciting and complex projects across the world and bring the same level of expertise to our local communities. We are proud to be an international collective of innovative thinkers who work on the most complex problems. Unified under one strong brand, we use our local expertise, international reach and global scale to prepare our cities and environments for the future, connect communities and help societies thrive in built and natural ecosystems. True to our guiding principles, our business is built on four cornerstones: Our People, Our Clients, Our Operational Excellence and Our Expertise.

www.wsp.com

We are

• Passionate people doing purposeful and sustainable work that helps shape our communities and the future.
• A collaborative team that thrives on challenges and unconventional thinking.
• A network of experts channeling our curiosity into creating solutions for complex issues.

Inspired by diversity, driven by inclusion, we work with passion and purpose.

Working with Us

At WSP, you can access our global scale, contribute to landmark projects and connect with the brightest minds in your field to do the best work of your life. You can embrace your curiosity in a culture that celebrates new ideas and diverse perspectives. You can experience a world of opportunity and the chance to shape a career as unique as you.

Our Hybrid Working Module

With us, you can operate in a flexible, agile, yet structured work environment and follow a Hybrid Work Model.

• Maximize collaboration.
• Maintain product quality and cultural integrity.
• Balance community, collaboration, opportunity, productivity, and efficiency.

Health, Safety and Wellbeing

Our people are our greatest asset, and we prioritize a safe work environment. Health, safety, and wellbeing are integral to our culture, with each of us accountable for fostering a safe workplace through our “Making Health and Safety Personal” initiative. Our Zero Harm Vision drives us to reduce risks through innovative solutions, earning recognition for our global health and safety practices with the prestigious RoSPA Health and Safety Awards for six consecutive years.

Inclusivity and Diversity

WSP India is dedicated to fostering a sustainable and inclusive work environment where our greatest strength - Our People -feel valued, respected, and supported. We ensure an unbiased approach in hiring, promotion, and performance evaluation, regardless of age, gender identity, race, religion, sexual orientation, marital status, physical ability, education, social status, or cultural background.

Imagine a better future for you and a better future for us all.

Join our close-knit community of over 73,300 talented global professionals dedicated to making a positive impact. Together, we can make a difference in communities both near and far.

NOTICE TO THIRD PARTY AGENCIES

WSP does not accept unsolicited resumes from recruiters, employment agencies, or other staffing services. Unsolicited resumes include any resume or hiring document sent to WSP in the absence of a signed Service Agreement where WSP has expressly requested recruitment/staffing services specific to the position at hand. Any unsolicited resumes, including those submitted to hiring managers or other business leaders, will become the property of WSP and WSP will have the right to hire that candidate without reservation – no fee or other compensation will be owed or paid to the recruiter, employment agency, or other staffing service.