13 hours ago
Pune, IndiaStaff+
Responsibilities
- Lead hands-on engineering and architecture for end-to-end Deep Learning, GenAI, Agentic AI, multimodal, and applied ML solutions.
- Evaluate AI use cases for technical feasibility, effort, cost, risk, and production viability using fail-fast principles.
- Own reference architectures and design patterns for multimodal agentic systems involving planning, tool use, memory, grounding, and inter-agent communication.
- Design multi-agent systems, multimodal pipelines, SLM strategies, hybrid retrieval architectures, and knowledge-grounded solutions.
- Establish evaluation frameworks, golden datasets, regression suites, automated and human-in-the-loop evaluations, observability, safety guardrails, and hallucination controls.
- Lead red-teaming, adversarial testing, production-readiness reviews, GPU and accelerator operations, model serving, and AI lifecycle automation.
- Set standards for reliability, latency, cost, monitoring, drift detection, and incident response in regulated enterprise environments.
- Mentor engineers through code and architecture reviews and establish AI software development lifecycle frameworks.
- Engage client and stakeholder leadership on architecture, feasibility, risk, technical direction, effort estimation, and solution framing.
- Support pre-sales and solutioning for GenAI and Agentic AI opportunities.
Requirements
- Minimum 8 years of total hands-on software development or engineering experience.
- Minimum 3 years of hands-on experience building and deploying Deep Learning or AI systems in production.
- Strong hands-on experience with neural networks, Transformers, predictive modeling, embeddings, and vector search.
- Hands-on experience with LLMs, SLMs, RAG, Agentic RAG, agents, prompt engineering, grounding, multimodal architectures, and production GenAI solutions.
- Practical experience with fine-tuning or optimization techniques such as SFT, LoRA, QLoRA, RLHF, RLAIF, distillation, or quantization.
- Hands-on experience with multi-agent orchestration, planning, tool use, memory, and agentic workflows; experience with LangGraph, LlamaIndex, or AutoGen.
- Advanced Python and SQL skills with strong API and backend engineering experience using FastAPI, Flask, Django, or equivalent frameworks.
- Experience designing, developing, testing, and deploying AI/ML solutions in enterprise production environments.
- Hands-on experience with at least one major cloud platform: AWS, Azure, or GCP.
- Experience with MongoDB, NoSQL, vector databases, graph databases, or equivalent data platforms.
- Demonstrable GenAI or Agentic AI experience beyond basic API integrations or simple RAG implementations, such as multi-agent systems, custom fine-tuning, advanced RAG, or SLM deployments.
- Experience with commerce cloud ecosystems such as Salesforce and Adobe is preferred.
- Willingness to work from the Pune office at least 3 days per week.
Benefits
- Full-time permanent employment.
- Hybrid work structure requiring at least 3 days per week in the Pune office.
Tech Stack
Categories
About Dentsu
Dentsu is a global advertising and marketing services group that plans and buys media, develops creative campaigns, and builds data- and technology-led customer experiences for brands. It earns revenue from agency fees and media services across a network that includes Carat, iProspect, and Merkle. Founded in 1901 and headquartered in Tokyo, Dentsu is publicly traded in Japan and serves clients across many sectors worldwide.