TGS The Global Skills
https://theglobalskills.comAbout
TGS The Global Skills is a recruitment and staffing solutions company helping businesses hire the right talent across India.
We specialize in IT Recruitment and specialized technology hiring, including AI/ML, Data, Cloud, SAP, Software, Engineering, Sales, and Operations.
Our goal: Connecting the right talent with the right opportunity.
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Delhi
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Jobs at TGS The Global Skills
PythonResponsibilities
· Build and operate the agentic loop: trigger → orchestration → agent execution → output to JIRA → human accept/reject → next agent, across design, coding, review, and testing agents.
· Implement model routing and retry logic across a provider-agnostic model layer (e.g., Claude via AWS Bedrock, self-hosted or alternative models as cost/sovereignty hedges), including business-continuity fallback if a given provider becomes unavailable.
· Own token cost control and context window management — per-agent and per-run budgets, circuit breakers that halt runaway execution, and cost observability tied back to JIRA.
· Stand up and maintain observability, alerting, and monitoring across the agent fleet (e.g., Langfuse or equivalent), so agent health, cost, and quality are visible in real time.
· Implement agent governance and safety guardrails: deterministic pre/post hooks gating every LLM call, kill switches, prompt injection prevention and mitigation, and audit logging.
· Integrate the harness with JIRA as the system of record and other business systems as needed, ensuring every agent action, decision, and human override is tracked with no side channels.
· Pair directly with client engineers throughout — this is capability transfer, not black-box delivery. You'll document, demo, and hand over as you build.
· Work in outcome-based delivery stages (spike → architecture sign-off → build → pilot) with gated milestones tied to working software demos, not fixed artifact checklists.
· Participate actively in team discussion and design decisions — this team expects engineers to challenge ideas constructively and speak up, not defer silently.
Must-Have Experience
· Hands-on production experience building agentic systems(not tutorial-level or personal-project experience.) Candidates should be able to speak concretely about systems they've shipped.
· Practical experience with agentic frameworks such as LangChain, LangGraph, or equivalent orchestration frameworks.
· Experience with LLM orchestration and model routing across multiple providers/models, including fallback and retry design.
· Working knowledge of agent governance: guardrails, human-in-the-loop approval flows, kill switches, and audit trails.
· Practical understanding of prompt injection risks and mitigation techniques.
· Experience with token cost management and context window/memory handling at production scale — this is a named governance requirement for the engagement, not a nice-to-have.
· Strong Python (or equivalent) engineering background, comfortable working in AWS environments (Bedrock/AgentCore exposure a strong plus).
· Experience with observability/monitoring tooling for distributed or agentic systems (e.g., Langfuse, Datadog, or equivalent).
· Comfortable working with JIRA/Atlassian APIs or similar ticketing-system-of-record integrations.
Nice to Have
· Direct experience with AWS Bedrock AgentCore, Temporal (or similar workflow orchestration), or LiteLLM-style model gateways.
· Exposure to Cursor or other AI-native IDEs in a production engineering context.
· Experience with self-hosted open-weight models (e.g., DeepSeek, GLM) as cost or sovereignty hedges alongside commercial APIs.
· Financial services or other regulated-industry background.
· Familiarity with Claude Code, Claude Cowork, or Claude Skills.
Qualifications
· Bachelor's degree in Computer Science, Engineering, or equivalent practical experience.
· 3-5+ years in software/platform engineering, with a meaningful portion of that time specifically on agentic or LLM-orchestration systems (not general ML or data engineering alone).
Relevant Experience
· Already built this kind of system and can talk through the trade-offs from experience, not theory.
· Comfortable operating with ambiguity - technology choices (frameworks, specific models, tooling) are expected to evolve during the engagementand milestones are tied to outcomes rather than fixed deliverables.
· Will contribute opinions - quiet execution without a point of view is not a fit for this team.
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