[Hiring] AI/ML Engineer @Joblogic

Role Description

We are building Joblogic’s AI Agent Platform — a multi-tenant system for designing, versioning, evaluating, and running AI agents that work across email, voice, SMS, WhatsApp, and CRM channels on behalf of our customers. The platform is built on a LangGraph runtime with a full agent lifecycle: a prompt-driven agent builder, a tool and knowledge-base registry, human-in-the-loop review, an agent memory subsystem, and a rigorous evaluation harness backed by LangSmith and PromptFoo.

We are looking for a mid-level AI/ML Engineer to help us design, build, and continuously improve these agents. In this role you will own agent behaviour end to end — crafting and engineering prompts, wiring up tools and retrieval, and, most importantly, building the evaluations and datasets that prove an agent is doing the right thing before and after it ships. You will also bring applied machine learning depth: working with our execution and conversation data, building models and analyses that make agents smarter, and using platforms such as Databricks or AWS SageMaker to train, track, and serve them.

You will work closely with product, backend, and platform engineers, and your work will directly shape how tens of thousands of field-service businesses experience intelligent automation.

Qualifications

• Strong Python engineering skills, with experience building production services and clean, well-tested code.
• Hands-on experience building LLM-powered applications or AI agents — for example with LangChain / LangGraph, the OpenAI / Anthropic APIs, or comparable frameworks.
• Demonstrable prompt engineering skill: designing, iterating on, and versioning prompts.
• Practical experience with LLM evaluation: building datasets, defining rubrics/metrics, running evals, and interpreting results.
• A solid machine learning foundation: framing problems, feature engineering, training and evaluating models.
• Hands-on experience with a modern ML platform — Databricks or AWS SageMaker (or equivalent).
• Strong data analysis skills using Pandas, NumPy, and SQL.
• Understanding of retrieval-augmented generation (RAG): embeddings, vector/hybrid search, chunking, and grounding/faithfulness.
• Experience working with APIs, JSON schemas, and integrating third-party services from Python.
• Familiarity with both SQL and NoSQL databases.
• Committed to continuous learning, proactive problem-solving, and timely issue identification.
• Strong communicator, experienced in collaborating with cross-functional teams using tools such as Jira and Slack.
• Creative and innovative thinker, consistently contributing fresh ideas and solutions.

Requirements

• Build and improve AI agents — design agent behaviour on our LangGraph runtime, configure abilities, attach tools and knowledge bases, and take agents from draft through evaluation to production deployment.
• Prompt engineering — author, version, and systematically improve system prompts.
• Design and run evaluations — build datasets, author heuristic and LLM-judge rubrics, run offline evaluations and online scoring against live executions.
• Retrieval & knowledge (RAG) — build and tune retrieval-augmented generation over our knowledge bases.
• Tools & integrations — develop and integrate the tools agents call.
• Data analysis — analyse execution traces, conversation transcripts, and outcome data.
• Applied ML — build, train, evaluate, and deploy machine learning models.
• MLOps — use platforms such as Databricks or AWS SageMaker for feature engineering, experiment tracking, model training, and serving.
• Observability & quality — use tracing and monitoring to debug agent runs in production.
• Collaborate & ship — work in a cross-functional team using tools such as Jira and Slack, write clear documentation, and ship iteratively.

Benefits

• Professional Working environment
• Market Competitive Salary
• Life Insurance & Medical Insurance (Including Family)
• OPD
• Provident Fund
• Gym Facility
• Maximum 45 Weekly Hours (Monday–Friday)
• Remote Working (During Pandemic Situation)
• Company trip
• 29 Annual Leaves
• 8 Sick & uncapped Compassionate Leaves (As per Company Policy)