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Coretek
Coretek
51 – 200 Employees
H1B Visa SponsorConsultingHealthcareManufacturing
Coretek is a Microsoft Azure Expert Managed Service Provider and cloud technology consultancy serving organizations across government, healthcare, manufacturing, and financial services. Its work spans cloud migration, application modernization, DevOps automation, workspace management, cybersecurity, and AI solutions built with Azure OpenAI and machine learning technologies. Coretek also delivers security and observability services using platforms such as Palo Alto and Dynatrace, alongside partnerships involving Imprivata and Citrix. The company’s multidisciplinary team helps clients modernize infrastructure, strengthen security, and develop practical cloud and AI capabilities.
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Senior Agentic AI Developer at Coretek — Hybrid India

Build reliable, secure Azure AI agents for Coretek clients, spanning RAG, tool integrations, evaluation, observability, and production delivery.

INIndiaHybridFull-timeSeniorEnglish RequiredAI EngineerMachine Learning Engineer
AzureDockerKubernetesPostgresPythonSQLTerraformVault

Description

  • Design, build, and launch Azure-based agentic AI applications with reasoning, tool calling, retrieval, memory, and human review checkpoints.
  • Develop RAG pipelines covering chunking, embeddings, indexing, hybrid and semantic search, reranking, and citation grounding.
  • Connect agents to client systems through APIs, databases, and MCP servers, including tool definitions and schemas.
  • Create evaluation harnesses and golden datasets to measure accuracy, groundedness, and task completion.
  • Add tracing, token and cost telemetry, latency budgets, fallback paths, and regression alerts to production agents.
  • Develop safeguards for prompt injection, content filtering, PII, output validation, and human approval boundaries.
  • Improve cost and latency through model selection, prompt caching, context management, batching, and model routing.
  • Use version control, code review, automated testing, CI/CD, and prompt and model versioning practices.
  • Containerize and deploy agent services while managing builds, releases, scaling, and runtime configuration.
  • Set technical direction for client engagements and support the growth of junior engineers.
  • Collaborate directly with clients to define agent use cases and evaluate model capabilities and limitations.
  • Work with project managers, data engineers, and application teams to deliver complete solutions and document them for ongoing operation.

Requirements

  • Seven or more years in software, data, or ML engineering, including at least two years building LLM-based or agentic applications used by real users.
  • Strong practical Python development experience, including testing, packaging, and code review.
  • Hands-on experience with LLM APIs and agent frameworks such as Microsoft Agent Framework, Azure AI Foundry, Azure OpenAI, or LangGraph.
  • Working knowledge of tool and function calling, structured outputs, planning and reflection loops, multi-agent handoffs, and deterministic workflows.
  • Experience building RAG systems with vector or hybrid search stores such as Azure AI Search, PostgreSQL with pgvector, or Cosmos DB.
  • Prompt engineering experience spanning system prompts, few-shot strategies, context-window management, and systematic evaluation-set iteration.
  • Production Docker and Kubernetes experience, including images, configuration, secrets, and workload deployment and scaling on AKS or an equivalent platform.
  • Experience deploying and operating Azure services such as AKS, Container Apps, Azure Functions, or App Service.
  • Strong API and data fundamentals, including REST, asynchronous programming, SQL, and JSON Schema design.
  • Experience with Git-based development workflows and CI/CD delivery.
  • Excellent communication skills, with the ability to explain complex technical topics and set realistic expectations around AI capabilities and risks.
  • Advanced analytical and debugging ability, including investigation of non-deterministic agent failures.
  • Substantial experience working with customers in a consultative technical setting.
  • Experience developing MCP servers or clients.
  • Familiarity with LLM observability and evaluation tools such as Azure AI Foundry evaluations, LangSmith, or OpenTelemetry tracing.
  • Familiarity with Azure networking, identity, and security, including Managed Identity, Key Vault, and Private Endpoints.
  • Experience with fine-tuning, distillation, or deploying small language models.
  • Experience with Microsoft Fabric, Azure Databricks, or Azure Synapse.
  • Experience with document intelligence and multimodal inputs, including Azure AI Document Intelligence, vision, or speech.
  • Experience in regulated environments involving HIPAA, SOC 2, GDPR, or the DPDP Act.
  • Infrastructure-as-code experience with Terraform, Bicep, or Helm.
  • Demonstrated ability to manage multiple client projects and deliver high-quality work on schedule.
  • Experience using Azure DevOps or GitHub for source control and pipelines.

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