skilled and innovative AI Development Platform Engineer Advanced Technology team Generative AI (GenAI) LLMs vector databases cloud-native platforms Key Responsibilities
- Architect and develop reusable tooling and self-service platforms to streamline enterprise AI solution deployment.
- Build scalable frameworks to support Generative AI use cases using both pre-trained and fine-tuned LLMs.
- Partner with developers, ML engineers, and DevOps teams to improve platform services, developer experience, and operational excellence.
- Use Kubernetes /OpenShift to manage and orchestrate containerized AI workloads in production environments.
- Implement GitOps deployment strategies with tools like Helm , Kustomize , ArgoCD , and JFrog Artifactory.
- Integrate and manage vector databases to support embedding-based search and RAG (retrieval-augmented generation) patterns.
- Drive platform-level architectural decisions covering authentication , state management , observability , and system reliability.
- Advocate for and integrate AI agent frameworks such as Langchain and LangGraph.
- Maintain comprehensive observability using tools like Grafana , Prometheus , Loki , and OpenTelemetry.
- Engage in Agile ceremonies and promote a DevOps-first, collaborative engineering culture.
Required Qualifications
- 5 years of hands-on software engineering experience, with a strong focus on backend or platform development.
- Proficient in Python (e.g., Flask , FastAPI) or similar modern programming languages.
- Deep understanding of RESTful APIs , microservices architecture, and scalable system design.
- Expertise in Kubernetes (preferably OpenShift), container lifecycle, and orchestration best practices.
- Strong background in CI/CD , DevOps , and GitOps , with experience in tools like Jenkins , ArgoCD , and Terraform.
- Familiar with SQL/NoSQL databases , Kafka , Redis, and event-driven architectures.
- Experience with OAuth 2.0, secure development practices, and compliance requirements.
- Knowledge of multiprocessing , multithreading , async I/O , and backend performance tuning.
- Understanding of ML/DL concepts , including familiarity with TensorFlow or PyTorch.
- Exposure to cloud-native design, SRE principles, and observability best practices.
Preferred Qualifications
- Hands-on experience with Generative AI and LLMs (e.g., GPT , LLaMA , Hugging Face).
- Experience implementing AI agents , Agentic Orchestration , and Multi-Agent Workflows.
- Familiarity with Langchain , LangGraph , and modern vector database technologies (e.g., Pinecone , FAISS , Weaviate).
- Understanding of ModelOps/LLMOps/MLOps pipelines and lifecycle management.
- Experience deploying in hybrid or multi-cloud environments using Blue/Green or Canary strategies.
- Proficiency in building and maintaining both stateful and stateless systems.
Educational Requirements
- Bachelor's or Master's Degree in Computer Science , Artificial Intelligence , Machine Learning , or a related field --- or equivalent professional experience.
