AI Architect / Lead Engineer (Agentic AI Platform)
TaskUs · Mumbai Metropolitan Region
- Hybrid
- Full-time
- $150,000 / year
- Mumbai Metropolitan Region
Job highlights
- Build hybrid AI platform across clouds.
- Own end-to-end architecture and implementation.
- Design systems, write code, lead team.
- Focus on scalability, privacy, and security.
- Hands-on builder-leader role with impact.
About the role
About TaskUs
TaskUs is a provider of outsourced digital services and next-generation customer experience to fast-growing technology companies, helping its clients represent, protect and grow their brands. Leveraging a cloud-based infrastructure, TaskUs serves clients in the fastest-growing sectors, including social media, e-commerce, gaming, streaming media, food delivery, ride-sharing, HiTech, FinTech, and HealthTech.
The People First culture at TaskUs has enabled the company to expand its workforce to approximately 45,000 employees globally. Presently, we have a presence in twenty-three locations across twelve countries, which include the Philippines, India, and the United States.
It started with one ridiculously good idea to create a different breed of Business Processing Outsourcing (BPO)! We at TaskUs understand that achieving growth for our partners requires a culture of constant motion, exploring new technologies, being ready to handle any challenge at a moment's notice, and mastering consistency in an ever-changing world.
What We Offer
At TaskUs, we prioritize our employees' well-being by offering competitive industry salaries and comprehensive benefits packages. Our commitment to a People First culture is reflected in the various departments we have established, including Total Rewards, Wellness, HR, and Diversity. We take pride in our inclusive environment and positive impact on the community. Moreover, we actively encourage internal mobility and professional growth at all stages of an employee's career within TaskUs. Join our team today and experience firsthand our dedication to supporting People First.
Role: AI Architect / Lead Engineer (Agentic AI Platform)
Experience: 7-15 years
Location: Flexible / Hybrid
About The Role
We are building a Hybrid Agentic AI Platform that runs across AWS, multi-cloud, and customer environments (VPC/on-prem). This role will own the end-to-end architecture AND implementation of the platform-spanning data ingestion, context building (RAG), agent orchestration, QA automation, and secure enterprise deployment.
This is a builder-first leadership role: you will design systems, write production code, and guide a small team to deliver a scalable, privacy-first AI platform.
Responsibilities
Architecture & System Design
- Define end-to-end architecture: ingestion → processing → RAG → agents → QA scoring → insights
- Design hybrid deployment models (SaaS, in-VPC, on-prem)
- Establish patterns for multi-tenancy, isolation, and scalability
- Make key build vs buy decisions (LLMs, vector DBs, orchestration)
AI / Agentic Systems
- Design and implement agent orchestration frameworks
- Build RAG pipelines (chunking, embeddings, retrieval, re-ranking)
- Integrate LLMs (managed/private) with no-retention and guardrails
- Define evaluation frameworks (quality, hallucination checks, QA scoring)
Security & Data Privacy
- Implement data-in-place architectures (compute-to-data, VPC access)
- Design for PII handling, masking, and auditability
- Ensure compliance-ready patterns (SOC2, GDPR-style controls)
Platform Engineering
- Build core services/APIs powering workflows and integrations
- Design event-driven and microservices architectures
- Ensure reliability, observability, and performance at scale
Team Leadership
- Lead and mentor engineers (AI, data, backend, FDE)
- Set coding standards, architecture principles, and best practices
- Work closely with customers on complex deployments when needed
Required Skills
Core Engineering
- Strong programming in Python (plus Node.js/Java is a bonus)
- Deep experience with distributed systems & system design
- Hands-on with APIs, microservices, and event-driven systems
AI / GenAI
- Production experience with LLMs / GenAI systems
- Strong understanding of: RAG architectures, Embeddings & vector search, Prompting and agent workflows
- Experience with LLM platforms (AWS Bedrock, open-source models, etc.)
Cloud & Platform
- Deep experience with AWS (VPC, IAM, Lambda, ECS/EKS, S3)
- Experience designing secure enterprise deployments
- Familiarity with Docker, Kubernetes, Terraform
Nice to Have
- Experience with agent frameworks (LangChain, LangGraph, etc.)
- Multi-cloud experience (Azure/GCP)
- Experience with contact center / QA automation domains
- Knowledge of data engineering pipelines
- Exposure to LLM evaluation and guardrails frameworks
What Makes You a Great Fit
- You are equally comfortable whiteboarding architecture and writing code
- You have built 0→1 systems and scaled them
- You make pragmatic decisions, not over-engineered ones
- You thrive in ambiguity and move fast with ownership
Impact
- Define and build the core platform architecture
- Accelerate MVP → production for enterprise customers
- Establish the technical foundation for a category-defining AI platform
Success in 90 Days
- Designed and validated reference architecture
- Built core RAG + agent orchestration pipeline
- Enabled first customer deployment (VPC or hybrid)
- Established engineering standards and velocity
What This Role Is Not
- Not a pure architect who only creates diagrams
- Not a research-only AI/ML role
- Not detached from customers or real-world constraints
This is a hands-on builder-leader role
How We Partner To Protect You:
TaskUs will neither solicit money from you during your application process nor require any form of payment in order to proceed with your application. Kindly ensure that you are always in communication with only authorized recruiters of TaskUs.
DEI:
In TaskUs we believe that innovation and higher performance are brought by people from all walks of life. We welcome applicants of different backgrounds, demographics, and circumstances. Inclusive and equitable practices are our responsibility as a business. TaskUs is committed to providing equal access to opportunities. If you need reasonable accommodations in any part of the hiring process, please let us know.
We invite you to explore all TaskUs career opportunities and apply through the provided URL https://www.taskus.com/careers/.
TaskUs is proud to be an equal opportunity workplace and is an affirmative action employer. We celebrate and support diversity; we are committed to creating an inclusive environment for all employees. TaskUs people first culture thrives on it for the benefit of our employees, our clients, our services, and our community.
Req Id: R_2604_5075_1
Posted At: Fri Apr 10 2026 00:00:00 GMT+0000 (Coordinated Universal Time)
Key skills/competency
- AI Architect
- Lead Engineer
- Agentic AI Platform
- Python
- AWS
- RAG
- LLMs
- GenAI
- Distributed Systems
- System Design
Skills & topics
- AI Architect
- Lead Engineer
- Agentic AI Platform
- Python
- AWS
- RAG
- LLMs
- GenAI
- Distributed Systems
- System Design
- Platform Engineering
- Team Leadership
- Cloud Computing
- Microservices
- Event-driven Architecture
How to get hired
- Tailor your resume: Highlight Python, AWS, RAG, and LLM experience.
- Showcase leadership: Emphasize building 0→1 systems and team mentoring.
- Demonstrate hands-on skills: Detail your experience writing production code and system design.
- Address hybrid deployment: Explain your understanding of SaaS, VPC, and on-prem solutions.
- Prepare for technical interviews: Be ready to discuss system architecture and AI/ML concepts.
Technical preparation
Behavioral questions
Frequently asked questions
- What is the primary focus of the AI Architect / Lead Engineer role at TaskUs?
- The AI Architect / Lead Engineer role at TaskUs focuses on designing, building, and implementing a Hybrid Agentic AI Platform. This involves end-to-end ownership of the platform, from data ingestion and RAG to agent orchestration and secure enterprise deployment, combining architectural design with hands-on coding and team leadership.
- What kind of experience is required for the AI Architect / Lead Engineer position at TaskUs?
- TaskUs requires 7-15 years of experience for this role, with a strong emphasis on core engineering skills like Python programming, distributed systems, and system design. Significant production experience with LLMs/GenAI, RAG architectures, and AWS cloud services is also crucial. Experience with agent frameworks and multi-cloud environments is a plus.
- How does TaskUs approach security and data privacy in its AI platform?
- TaskUs implements security and data privacy through data-in-place architectures, PII handling and masking, and auditability features. The platform is designed with compliance-ready patterns like SOC2 and GDPR-style controls to ensure a privacy-first approach.
- What are the expected outcomes for a successful AI Architect / Lead Engineer within the first 90 days at TaskUs?
- Within the first 90 days, a successful candidate is expected to have designed and validated a reference architecture, built the core RAG and agent orchestration pipeline, enabled the first customer deployment, and established engineering standards and velocity.
- What type of work arrangement does the AI Architect / Lead Engineer role at TaskUs offer?
- The AI Architect / Lead Engineer role at TaskUs offers a flexible/hybrid work arrangement, indicating a blend of remote and on-site work, or the ability to work from various locations while collaborating with teams.
- Does TaskUs encourage internal mobility and professional growth for this role?
- Yes, TaskUs emphasizes internal mobility and professional growth. They encourage employees to develop their careers within the company and offer support through various departments focused on employee well-being and development.
- What specific cloud technologies are most important for the AI Architect / Lead Engineer at TaskUs?
- Deep experience with AWS services such as VPC, IAM, Lambda, ECS/EKS, and S3 is essential. Familiarity with Docker, Kubernetes, and Terraform is also expected. Experience with multi-cloud environments like Azure and GCP is considered a plus.