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Description and Requirements
Description and Requirements
- Design, build, and deploy production-ready Generative AI and Agentic AI solutions for GTM business use cases.
- Build AI agents, copilots, and automation workflows to improve sales productivity, decision-making, and operational efficiency.
- Develop AI workflows that can retrieve trusted information, use tools, call APIs, reason over business context, and generate actionable recommendations.
- Build and maintain LLM-powered applications using Python and modern AI engineering practices.
- Implement Retrieval-Augmented Generation architectures using enterprise data, approved knowledge sources, vector search, and source-grounded responses.
- Develop prompts as managed, versioned, testable, and reusable engineering assets.
- Use agent and orchestration frameworks such as LangGraph, LangChain, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, or similar technologies.
- Work with vector databases and search platforms such as Pinecone, Weaviate, FAISS, Milvus, Azure AI Search, OpenSearch, or similar technologies.
- Integrate AI applications with enterprise systems such as CRM, data warehouses, APIs, knowledge bases, collaboration tools, and GTM platforms.
- Build evaluation frameworks to test AI outputs for accuracy, groundedness, relevance, completeness, safety, and business usefulness.
- Implement monitoring and feedback loops to improve model performance, prompt quality, retrieval quality, cost, latency, and user experience.
- Design guardrails to reduce hallucinations, prevent unsafe outputs, protect sensitive data, and ensure responsible AI usage.
- Partner with IT, data engineering, architecture, security, and business teams to move AI solutions from prototype to production.
- Create reusable AI patterns, prompt libraries, evaluation templates, and implementation standards that can scale across multiple GTM AI use cases.
- Deliver trusted, secure, scalable, and useful AI solutions that improve productivity, decision-making, and business outcomes across the GTM organization.
- Bachelor’s degree in Computer Science, Engineering, Data Science, AI/ML, or a related technical field, or equivalent practical experience.
- 6+ years of experience in software engineering, data engineering, machine learning engineering, AI engineering, or related technical roles.
- 2+ years of hands-on experience building LLM, Generative AI, or Agentic AI applications.
- Strong programming experience in Python.
- Experience building production-grade applications, APIs, services, automation workflows, or data-driven solutions.
- Hands-on experience with LLM application development, prompt engineering, RAG architectures, vector search, tool use, and agent orchestration.
- Experience with AI frameworks such as LangGraph, LangChain, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, or similar tools.
- Experience with enterprise AI platforms such as Microsoft Copilot Studio, Azure AI Foundry, Azure OpenAI, AWS Bedrock, Google Vertex AI, or similar platforms.
- Experience with vector databases or retrieval platforms such as Pinecone, Weaviate, FAISS, Milvus, Azure AI Search, OpenSearch, or similar technologies.
- Strong understanding of hallucination mitigation, grounding techniques, prompt injection risks, AI safety, guardrails, evaluation, and monitoring.
- Experience integrating AI solutions with APIs, databases, data warehouses, enterprise systems, and business applications.
- Ability to translate business problems into practical AI solution designs.
- Strong communication skills and ability to work effectively with both technical and non-technical stakeholders.
- Hands-on builder mindset with the ability to move beyond demos and build enterprise-grade AI solutions that are grounded, tested, monitored, secure, and scalable.
- Experience building AI solutions for Sales, Revenue Operations, Customer Success, Marketing, Partner, or other GTM teams.
- Experience with CRM platforms such as Salesforce.
- Experience with cloud platforms such as Azure, AWS, or Google Cloud.
- Experience with data platforms such as Snowflake, Databricks, BigQuery, or similar technologies.
- Experience with AI observability, logging, tracing, model evaluation, feedback collection, and performance monitoring.
- Experience with prompt lifecycle management, including prompt versioning, testing, approval workflows, regression testing, and performance tracking.
- Experience with CI/CD, Git, DevOps, containers, secure software development, and production deployment practices.
- Familiarity with enterprise security, role-based access control, data privacy, governance, compliance, and responsible AI standards.
- Exposure to designing reusable AI engineering patterns that can scale across multiple enterprise use cases.
Our commitment to you!
BMC’s culture is built around its people. We have 6000+ brilliant minds working together across the globe. You won’t be known just by your employee number, but for your true authentic self. BMC lets you be YOU!
If after reading the above, You’re unsure if you meet the qualifications of this role but are deeply excited about BMC and this team, we still encourage you to apply! We want to attract talents from diverse backgrounds and experience to ensure we face the world together with the best ideas!
BMC is committed to equal opportunity employment regardless of race, age, sex, creed, color, religion, citizenship status, sexual orientation, gender, gender expression, gender identity, national origin, disability, marital status, pregnancy, disabled veteran or status as a protected veteran. If you need a reasonable accommodation for any part of the application and hiring process, visit the accommodation request page.
(Returnship@BMC)
Had a break in your career? No worries. This role is eligible for candidates who have taken a break in their career and want to re-enter the workforce. If your expertise matches the above job, visit to https://bmcrecruit.avature.net/returnship know more and how to apply.