Posted:9 hours ago|
Platform:
On-site
Full Time
• Implement multi-step reasoning, tool-calling, memory structures, and task automation using agent-based architectures.
• Build shared components that abstract domain-specific logic behind configurable templates and rules.
• Collaborate with architects to ensure services follow best practices for performance, reliability, and maintainability.
• Participate in model evaluation, fine-tuning, benchmarking, and experimentation.
• Implement confidence scoring, model monitoring, drift detection, and quality assurance practices.
• Collaborate with MLOps engineers to package, deploy, scale, and monitor ML models in production.
• Work closely with backend, DevOps, product, and domain teams to integrate ML capabilities into the platform.
• Mentor junior developers and help them grow in ML engineering best practices.
• Participate actively in design reviews, code reviews, and platform-level architecture discussions.
• Communicate technical ideas clearly and work collaboratively in a cross-functional environment.
• Document models, prompts, APIs, workflows, experiments, and platform components.
• Follow best practices in version control, testing, evaluation, and observability for ML components.
• Contribute to continuously improving engineering processes, coding standards, and platform guidelines.
• Solid understanding of NLP techniques and experience working with embeddings, RAG, or prompt engineering.
• Ability to build ML-driven microservices and APIs for consumption by other teams.
• Familiarity with cloud platforms (AWS/GCP/Azure) and containerization (Docker/Kubernetes).
• Strong analytical, debugging, and problem-solving skills.
• Ability to guide and mentor junior team members on technical tasks.
• Hands-on experience with vector databases (Pinecone, Weaviate, Milvus, etc.).
• Knowledge of distributed systems, event-driven architectures, or real-time inference pipelines.
• Exposure to MLOps tools such as MLflow, Weights & Biases, KServe, Triton Server.
• Basic knowledge of domain-driven design or building platform-level shared services.
• Experience in designing evaluation frameworks or automated testing for LLMs/agents.
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