TraceLink is Hiring Agentic AI Engineer | Pune | Freshers-2 Years Exp

Job Description

TraceLink's Agentic AI Engineer opening is one of the more genuinely rigorous early-career AI roles worth flagging on this site — unlike several fresher "AI" listings elsewhere that turn out to be coordination or adoption-support roles, this one is squarely 𝗵𝗮𝗻𝗱𝘀-𝗼𝗻 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜/𝗚𝗲𝗻𝗔𝗜 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: building multi-agent workflows, RAG pipelines, and testing strategies for AI systems that behave non-deterministically. If you're specifically looking for technical AI engineering work rather than AI-adjacent project management, this is a genuine match.

TraceLink itself operates in a specific and less commonly covered niche — 𝘀𝘂𝗽𝗽𝗹𝘆 𝗰𝗵𝗮𝗶𝗻 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗹𝗶𝗳𝗲 𝘀𝗰𝗶𝗲𝗻𝗰𝗲𝘀 𝗮𝗻𝗱 𝗵𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲, describing itself as an "Agentic Business Network" connecting over 300,000 participants globally. This gives the role real-world enterprise stakes: the AI agents you'd help build coordinate actual supply chain operations for pharmaceutical and healthcare companies, not a consumer-facing chatbot — worth noting since this domain (life sciences supply chain) has genuine regulatory and reliability requirements that shape how seriously "testing and validation for non-deterministic AI behavior" is treated here.

The role explicitly welcomes candidates without deep prior AI specialization — the required qualifications ask for 𝗴𝗲𝗻𝗲𝗿𝗮𝗹 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 (APIs, distributed systems basics, data structures/algorithms) paired with interest or exposure to GenAI concepts, rather than demanding hands-on production LLM experience. This is a notably more accessible bar than some other agentic AI listings covered on this site, which often require demonstrated GitHub projects as a hard prerequisite — here, coursework or academic exposure to RAG/agent concepts is explicitly listed as sufficient at the "required" level, with hands-on project experience positioned as preferred, not mandatory.

𝗢𝘂𝗿 𝗧𝗮𝗸𝗲:

- 𝗚𝗼𝗼𝗱 𝗳𝗼𝗿: CS/AI/Data Science graduates with solid Python or Java fundamentals and genuine curiosity about GenAI/agentic systems, even without a polished personal AI project — this listing is more accessible than several other agentic AI roles that demand demonstrated builds upfront

- 𝗡𝗼𝘁 𝗶𝗱𝗲𝗮𝗹 𝗳𝗼𝗿: those without core software engineering fundamentals (APIs, data structures, debugging) — the listing is clear that general engineering competence matters as much as AI-specific knowledge

- 𝗦𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻 𝗱𝗶𝗳𝗳𝗶𝗰𝘂𝗹𝘁𝘆: moderate — the "required" bar is genuinely fresher-friendly, but competition for GenAI-labeled roles at established companies tends to be high regardless of stated requirements, so any preferred-qualification experience (RAG, SLM fine-tuning) would help you stand out

- 𝗖𝗮𝗿𝗲𝗲𝗿 𝘁𝗿𝗮𝗷𝗲𝗰𝘁𝗼𝗿𝘆: commonly leads into AI/ML Engineer, Applied AI Engineer, or Agentic Systems Engineer roles, with TraceLink's specific niche (life sciences supply chain AI) being a distinctive credential for future roles in either AI engineering broadly or supply chain tech specifically

𝗖𝗼𝗺𝗽𝗲𝗻𝘀𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝗻𝘁𝗲𝘅𝘁: TraceLink hasn't disclosed pay for this role. Early-career Agentic AI/GenAI Engineer roles at established enterprise SaaS companies in Pune typically range around ₹8–13 LPA for candidates with 0-2 years of experience, based on general market data for comparable applied AI engineering roles — this is an estimate only, and TraceLink's position as an established, Gartner-recognized industry leader could position actual offers within or above that range.

𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆: Since the required qualifications explicitly accept coursework or academic exposure to GenAI concepts (not just hands-on project work), don't self-select out if you lack a polished personal AI project — instead, be ready to speak clearly about what you've learned conceptually (agent workflows, tool calling, multi-step reasoning) even from coursework, while highlighting any personal experimentation you have with RAG or agent frameworks as a genuine differentiator given it's explicitly called out as preferred.

Roles & Responsibilities

The responsibilities balance building AI-specific systems (agentic workflows, RAG pipelines, evaluation strategies) with standard software engineering practice (backend services, code reviews, documentation) — meaning this role develops both general engineering skill and specialized AI competency simultaneously.

- Support design and implementation of agentic AI/GenAI systems automating supply chain workflows

- Build and maintain backend services and integrations using Python and/or Java

- Contribute to multi-agent workflows: tool execution, routing, agent collaboration, task orchestration

- Assist in creating testing/validation strategies for non-deterministic AI systems, including evaluation datasets and regression testing

- Help implement and improve RAG pipelines, grounding strategies, and retrieval quality

- Contribute to experimentation with lightweight SLM fine-tuning and RL-inspired improvement loops

- Partner with product/domain teams to translate supply chain needs into working software

Qualifications & Eligibility

The degree requirement is specifically technical, but the experience bar is genuinely open — internships and co-ops explicitly count toward the 0-2 years range.

- Master's/Bachelor's in Data Science, AI, Machine Learning, Computer Science, or a closely related field

- 0–2 years of professional experience in software/AI/ML engineering (internships/co-ops count) or equivalent

- Strong Python and/or Java programming skills, including production-quality code

- Familiarity with cloud platforms (AWS, GCP, Azure) — academic or personal project experience acceptable

Skills Required

Core engineering fundamentals are treated as equally important to GenAI-specific knowledge — this isn't a role purely gated on AI expertise.

- Interest or exposure to GenAI concepts: LLMs, agent workflows, tool calling, multi-step reasoning

- Core engineering fundamentals: APIs/services, basic distributed systems, debugging, data structures & algorithms

- Preferred: RAG, embedding-based search, hybrid search, reranking exposure

- Preferred: SLM fine-tuning or NLP model training/adaptation experience

- Preferred: interest in supply chain domains (logistics, manufacturing, procurement)