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Agentic AI Engineer
TraceLink
Pune
0-2 years
1 day ago
$7.2K–12.0K/yr
Full-time
Onsite
Skills Required
LLM
RAG
Gen AI
Fine-tuning
Python
Java
AWS
GCP
Azure
NLP
agent workflows
tool calling
multi-step reasoning
APIs
distributed systems
Description
TraceLink is hiring an early-career Agentic AI Engineer to help build AI-powered systems that automate and improve supply chain workflows. The role focuses on agentic AI, GenAI, RAG, testing, and production support in an enterprise environment.
Company: TraceLink, Inc
Role: Agentic AI Engineer
Location: APAC - India - Pune
Experience
- 0–2 years of professional experience in software engineering, AI engineering, or ML engineering; internships and co-ops count
- Strong programming skills in Python and/or Java
- Familiarity with cloud platforms such as AWS, GCP, or Azure
- Interest or exposure to Generative AI concepts such as LLMs, agent workflows, tool calling, or multi-step reasoning
- Understanding of APIs and services
- Understanding of basic distributed systems concepts
- Understanding of debugging and performance basics
- Understanding of data structures and algorithms
- Ability to learn quickly, take feedback well, and collaborate effectively in a team environment
Qualification
- Master’s degree in Data Science, Artificial Intelligence, Machine Learning, Computer Science, or a closely related discipline
- Bachelor’s degree in Data Science, Artificial Intelligence, Machine Learning, Computer Science, or a closely related discipline
Responsibilities
- Design and implement agentic AI and GenAI systems for supply chain workflow automation
- Build and maintain backend services and integrations using Python and/or Java
- Contribute to multi-agent workflows including tool execution, routing, agent collaboration patterns, and task orchestration
- Create testing and validation strategies for AI systems, including evaluation datasets, regression testing, and behavior monitoring
- Implement and improve knowledge base systems, including RAG pipelines, grounding strategies, and retrieval quality improvements
- Partner with product and domain teams to translate supply chain needs into working software
- Participate in code reviews, documentation, and operational support
- Work alongside experienced engineers and data scientists on real-world enterprise problems
Additional Responsibilities
- Contribute to experimentation with lightweight fine-tuning approaches for small language models
- Contribute to experimentation with reinforcement-learning-inspired improvement loops for NLP and GenAI tasks where applicable
- Support AI system reliability through experiments, evaluation improvements, and thoughtful engineering
- Take defined tasks such as building a new RAG retriever, improving evaluation coverage, or implementing a new agent tool and deliver a working solution with support from senior engineers
- Write clean, testable code and improve ability to debug real-world production issues
Nice To Have
- Coursework, projects, or hands-on experience with agentic or multi-step AI systems, including non-deterministic behavior patterns
- Exposure to designing knowledge base solutions such as Retrieval-Augmented Generation, embedding-based search, hybrid search approaches, reranking, or relevance evaluation
- Experience or academic background in fine-tuning small language models, training or adapting NLP models, or reinforcement learning concepts applied to language systems
- Exposure to event-driven or reactive systems
- Interest in supply chain domains such as logistics, manufacturing, or procurement
- Knowledge of the life sciences supply chain
More Skills
knowledge base systems, cloud platforms, services, debugging, performance basics, data structures, algorithms, evaluation datasets, regression testing, behavior monitoring, lightweight fine-tuning, small language models, SLMs, reinforcement learning, agentic AI, multi-agent workflows, routing, agent collaboration, task orchestration, embedding-based search, hybrid search, reranking, relevance evaluation, event-driven systems, reactive systems, supply chain
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