Senior Technical Lead - Generative AI
At a glance
Senior AI Engineering role at Weekday AI. Remote, India ยท full-time.
Pay not stated AI Engineering salary
aimlroles summary, based on the employer's posting.
What you'll do
- Design and build agentic AI and generative AI systems, taking them from early prototypes into production
- Create reasoning agents, tool-calling flows and multi-agent setups using orchestration frameworks or custom code
- Put RAG pipelines into production, covering document chunking, embeddings, vector search and hybrid retrieval
- Set architecture and operating standards for dependable LLM applications, including evaluation, monitoring and guardrails
- Guide AI/ML and backend engineers while staying hands-on in design reviews and complex technical work
What you bring
- At least ten years in software engineering, including four years working directly with AI/ML systems
- At least two years building and deploying LLM or agentic AI applications in production
- Strong Python skills and experience delivering distributed systems, APIs, microservices and cloud-based applications
- Deep hands-on knowledge of LLM development, RAG, embeddings, vector databases, prompt design and AI agents
- Experience leading technical decisions and mentoring engineers across teams
Who this fits
This senior, hands-on leadership role suits someone who can guide engineers while still tackling complex AI engineering work. Youโll work remotely from India and collaborate with product, data, platform, security and compliance teams.
From the employer
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๐ฆ๐ฎ๐น๐ฎ๐ฟ๐ ๐ฟ๐ฎ๐ป๐ด๐ฒ: ๐ฅ๐ ๐ฏ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ - ๐ฅ๐ ๐ฑ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ (๐ถ๐ฒ ๐๐ก๐ฅ ๐ฏ๐ฌ-๐ฑ๐ฌ ๐๐ฃ๐)
Experience: 10+ yrs
Location: Remote (India)
Job Type: Full-time
We are looking for a highly experienced Senior Technical Lead โ Agentic AI / Generative AI to own the architecture, technical direction, and delivery of production-grade AI solutions. This is a hands-on leadership role for someone who can move seamlessly from early-stage experimentation and prototyping to scalable enterprise production systems.
You will design and build LLM-powered agentic systems, RAG architectures, multi-agent workflows, and AI applications, while mentoring a team of AI/ML and backend engineers. You will also work closely with Product, Data, Platform, Security, and other stakeholders to turn emerging GenAI capabilities into reliable, scalable, and business-ready solutions.
Requirements
Key Responsibilities
AI Architecture & Development
- Architect and develop Agentic AI and Generative AI systems from concept through production.
- Build multi-step reasoning agents, tool/function-calling workflows, and multi-agent architectures using frameworks such as LangGraph, AutoGen, CrewAI, or custom orchestration.
- Design and productionize scalable RAG pipelines, including chunking, embeddings, vector search, and hybrid retrieval.
- Evaluate and select foundation models based on performance, accuracy, latency, cost, and business requirements.
- Develop strategies for prompt engineering, model routing, fine-tuning, and optimization.
Production Engineering
- Own technical architecture decisions for scalable, reliable, and cost-efficient LLM applications.
- Establish engineering standards covering testing, evaluation, observability, guardrails, hallucination mitigation, and production monitoring.
- Design APIs, microservices, and cloud-native architectures supporting AI applications at scale.
- Drive AI/LLMOps practices across model lifecycle management, deployment, monitoring, and continuous improvement.
Technical Leadership
- Lead, mentor, and develop AI/ML and backend engineers.
- Conduct technical design reviews, architecture discussions, and code reviews.
- Establish engineering best practices and promote high standards for production AI development.
- Provide technical direction while remaining actively involved in complex engineering problems.
Cross-Functional Collaboration
- Partner with Product, Data Science, Platform, Security, and Compliance teams to deliver AI solutions aligned with business objectives.
- Ensure AI systems meet appropriate privacy, security, compliance, and responsible-AI requirements.
- Communicate complex technical concepts clearly to senior leadership and business stakeholders.
- Represent the AI engineering function in strategic discussions around GenAI technology and roadmap decisions.
What's Makes You a Great Fit
- 10+ years of overall software engineering experience, including 4+ years working directly with AI/ML systems.
- At least 2+ years of hands-on experience building and deploying LLM-based or agentic AI applications in production.
- Deep expertise in LLM application development, RAG, embeddings, vector databases, prompt engineering, and AI agents.
- Practical experience with multi-agent systems, tool/function calling, memory management, planning, and reasoning workflows.
- Strong Python and software engineering fundamentals with experience building scalable, distributed, production-grade systems.
- Experience with APIs, microservices, cloud-native architecture, and at least one major cloud platform such as AWS, Azure, or GCP.
- Hands-on experience with MLOps/LLMOps tools such as MLflow, LangSmith, Weights & Biases, or equivalent platforms.
- Working knowledge of LLM fine-tuning and evaluation techniques, including LoRA/PEFT, RLHF concepts, and offline/online evaluation frameworks.
- Proven ability to provide technical leadership, mentor engineers, own architecture decisions, and collaborate across teams.
- Strong communication skills with the ability to translate complex technical concepts into clear business and executive-level discussions.
Good to Have
- Experience deploying and fine-tuning open-source models such as Llama or Mistral, alongside proprietary models/APIs.
- Contributions to AI/GenAI open-source projects, technical publications, or conference presentations.
- Experience building AI solutions within regulated industries such as finance, healthcare, or telecom.
- Knowledge of AI guardrails, red-teaming, responsible AI, and model safety/evaluation frameworks.
- Previous formal people-management experience.