AI/ML Engineer – Agentic AI & LLM Enthusiast

Altera•San Jose, CA

About The Position

We are seeking a highly skilled ML/AI Engineer passionate about Agentic AI architectures, LLM ecosystems, and scalable backend systems. The ideal candidate blends exceptional academic achievement with hands-on experience building production-grade AI agents, multi-agent workflows, and distributed systems in real-world enterprise settings. You will design and develop next-generation AI systems—hyper-intelligent agents, secure LLM workflows, data privacy defenses, evaluators, and scalable multi-tenant AI platforms powering mission-critical applications. This role offers the opportunity to work on high‑impact systems operating at enterprise scale.

Requirements

Bachelor’s degree in Computer Science or related field, and 3+ years of experience in the following:
3+ years of experience in ML/AI, backend engineering, or agentic LLM systems (industry or internships).
LLM frameworks: LangChain, LangGraph, RAG, embeddings, transformers
Languages: Python, Go, Typescript, C++, Bash
Cloud & Infrastructure: AWS/GCP, K8s, Docker, CI/CD
Data systems: SQL, NoSQL, ElasticSearch, vector DBs
Experience building production-grade AI agents or pipelines.

Nice To Haves

Developed or contributed to state-of-the-art AI pipelines in enterprise environments.
Experience with AI privacy & security, prompt guardrails, and model governance.
Experience deploying large-scale AI search, data processing, or distributed ML workflows.
Published research or contributions to open-source ML tooling.
Experience in high‑pressure engineering environments (FAANG, YC startups, or similar preferred).

Responsibilities

Architect and productionize Agentic AI systems using LangGraph, LangChain, and modern LLM APIs (OpenAI, Anthropic, Vertex, NVIDIA).
Build multi-agent workflows, including chunking pipelines, re-ranking, vector retrieval, classifier tuning, and guardrail enforcement.
Develop enterprise-grade AI privacy and security controls, including PII masking, leakage prevention, threat detection, and LLM response governance.
Implement RAG pipelines across large heterogeneous datasets (100GB+ scale).
Own LLMOps lifecycle: deployment, monitoring, evaluation, and observability.
Build reliable backend microservices in Python, Go, Typescript, C++, orchestrated via Kubernetes, Docker, Helm, and cloud platforms (AWS/GCP).
Design scalable data ingestion, async orchestration, Pub/Sub event systems, and workflow schedulers.
Integrate vector databases (FAISS, Weaviate, ElasticSearch, Redis, Postgres).
Develop secure and compliant systems aligned with IAM, RBAC, cloud security, and AI governance regulations.
Design multi-threaded and async processing systems for AI agents.
Implement checkpointing, backpressure management, temporal scheduling, and cluster-scaling mechanisms.
Monitor system health using Prometheus, Grafana, Kibana, structured logs.