UNKNOWN

FEED

HOUR

Track and evaluate the AI landscape new LLMs, agentic frameworks (LangGraph, Google ADK, CrewAI, AutoGen), retrieval methods, fine-tuning techniques, and emerging tooling. AI system architectures: multi-agent orchestration layers, RAG pipelines, hybrid retrieval systems (knowledge graphs + vector search), text-to-SQL engines, and real-time inference APIs.

AI Architecture & System Design 
 AI system architectures: multi-agent orchestration layers, RAG pipelines, hybrid retrieval systems (knowledge graphs + vector search), text-to-SQL engines, and real-time inference APIs. 
 Define and own technical blueprints for new AI products from data ingestion and embedding pipelines through to response generation, evaluation, and production monitoring. 
 Solve hard engineering problems: latency, precision/recall trade-offs, context window management, hallucination mitigation, and cost-efficient LLM usage at scale. 
 Make deliberate, well-documented architecture decisions with clear trade-off analysis (build vs. buy, framework selection, deployment topology). Implementation 
 Write production-quality code Python, SQL, API services across the full AI lifecycle: data qualification, model training, evaluation, containerised deployment, and API serving. 
 Build and own reusable, framework-quality components (chunking pipelines, retrieval layers, agent tool-calling modules) that accelerate team velocity. 
 Own CI/CD pipelines, Docker-based deployment, and production telemetry for AI services. AI Market Intelligence & Technology Strategy 
 Track and evaluate the AI landscape new LLMs, agentic frameworks (LangGraph, Google ADK, CrewAI, AutoGen), retrieval methods, fine-tuning techniques, and emerging tooling. 
 Translate AI market trends into actionable roadmap inputs surfacing opportunities for step change capability improvements before competitors do. Cross-Functional Technical Partnership 
 Partner closely with Product, Data Science, and Platform Engineering to align AI architecture with product direction, data constraints, and infrastructure capabilities. 
 Communicate complex technical trade-offs clearly to non-technical stakeholders translating architecture decisions into business impact narratives. Must-Have Experience 
 12+ years of hands-on experience in AI/ML engineering and data science, with significant depth in production system delivery. 
 Deep, working expertise in LLM application development: LangChain, LangGraph, tool-calling agents, RAG, prompt engineering, embedding pipelines, and hybrid retrieval. 
 Proven track record architecting and shipping multi-agent systems, knowledge graph-powered retrieval (Neo4j or equivalent), and real-time inference APIs. 
 Strong ML fundamentals: XGBoost, deep learning, NLP, time-series forecasting, propensity modelling, experimental design, and causal inference. 
 Experience delivering AI systems in regulated industries (financial services, cybersecurity, healthcare) with SOX, GDPR, or SOC 2 compliance awareness. 
 Expert-level Python and SQL; fluency with GCP, AWS, Docker, FastAPI, BigQuery, FAISS, and CI/CD tooling. Technical Depth 
 Ability to design hybrid retrieval architectures that balance precision (graph traversal) and semantic recall (vector similarity), with reranking layers not just off-the-shelf RAG. 
 Hands-on experience reducing LLM inference latency in production (e.g., redesigning pipelines from multi-minute to sub-30-second response times). QUALIFICATIONS 
 Master's or PhD in Computer Science, Operations Research, Statistics, or a related quantitative field 
 AWS Certified Machine Learning Engineer or GCP Professional ML Engineer certification. 
 Completion of an AI Strategy or AI Governance programme. 
 Prior experience at a data science / ML services firm, enterprise SaaS, or fintech where you shipped AI to external customers, not just internal tools. 
 Hands-on experience with Snowflake Cortex or comparable enterprise LLM deployment platforms. 
 Open-source contributions to AI/ML tooling, published technical writing, or conference presentations.

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eTeam Inc.

AI Architect

Amazon Web Services (AWS)

Application Programming Interface (API)

Artificial Intelligence (AI)

Artificial Intelligence (AI) Programming Languages

Blueprints

Competitive Analysis/Strategy

Computer Science

Continuous Deployment/Delivery

Continuous Integration

Cross-Functional

Data Modeling

Data Science

Deep Learning

Docker

Experiment Design

Finance

Financial Services

Forecasting

GCP (Good Clinical Practices)

Healthcare

Information Retrieval

Internet Security

Machine Tool

Market Trend Analysis

Natural Language Processing (NLP)

Open Source

Operations Research

Problem Solving Skills

Production Control

Production Systems

Productivity Management

Python Programming/Scripting Language

SQL (Structured Query Language)

Sarbanes-Oxley Act (SOX)

Shallow Parsing

Software Development

Software as a Service (SaaS)

Statistics

System Architecture

Technical Publications

Technical Strategy

Telemetry

jobdiva

OTHER

You will operate simultaneously as a product engineer (building compelling agent applications), a platform engineer (stress-testing hardware through realistic workloads), and a security engineer (implementing privacy-preserving inference via FHE hardware acceleration) to influence requirements conversations and go-to-market efforts in regulated sectors such as healthcare and finance. Design and build enterprise-grade multi-agent AI workflows in target verticals (healthcare, finance, legal) using LangGraph, CrewAI, and/or AutoGen, optimized to leverage advanced CXL memory and accelerator hardware.

Position OverviewApplication Software Engineers in this role build vertical AI agent systems that validate and prove the underlying hardware and software capabilities through real-world enterprise deployments. You will operate simultaneously as a product engineer (building compelling agent applications), a platform engineer (stress-testing hardware through realistic workloads), and a security engineer (implementing privacy-preserving inference via FHE hardware acceleration) to influence requirements conversations and go-to-market efforts in regulated sectors such as healthcare and finance.Key Responsibilities Design and build enterprise-grade multi-agent AI workflows in target verticals (healthcare, finance, legal) using LangGraph, CrewAI, and/or AutoGen, optimized to leverage advanced CXL memory and accelerator hardware. Architect agent systems that exploit ultra-long context windows (1M+ tokens) backed by multi-terabyte memory, enabling persistent long-term agent memory and cross-session state beyond typical GPU-based limits. Implement privacy-preserving inference pipelines using FHE hardware acceleration so agents can process sensitive data (e.g., medical or financial) without exposing plaintext to the inference engine. Build the agent workload benchmark suite used to validate hardware performance metrics (TTFT, throughput, KV-cache utilization) in coordination with the system software team. Develop agent security hardening features: least-privilege tool access, skill signing and attestation, behavioral anomaly detection, and prompt-injection defenses aligned with OWASP Top-10 LLM risks. Prototype multi-agent orchestration for reference deployments in priority regions, validating real multi-agent workloads on the platform. Collaborate with the system software team to surface agent-level requirements for KV-cache and OKC-style APIs, closing the feedback loop between application behavior and hardware optimization. Create technical documentation, reference architectures, and integration guides for enterprise and hyperscaler partners.Required Skills & Experience 6+ years in software engineering, including 2+ years building production multi-agent or agentic AI systems. Hands-on proficiency with at least two of: LangGraph, CrewAI, AutoGen, LlamaIndex Workflows, or comparable multi-agent orchestration frameworks. Demonstrated experience validating or benchmarking AI hardware through real-world agent workloads rather than only synthetic benchmarks. Strong Python engineering skills and experience deploying LLM inference services in containerized / cloud-native environments (e.g., vLLM, SGLang, Triton). Deep understanding of 1M+ token context optimization challenges: memory management, chunked processing, hierarchical summarization, and large-scale RAG. Working knowledge of FHE concepts and privacy-preserving ML, with the ability to integrate hardware-accelerated FHE libraries (such as OpenFHE or Concrete-ML) into inference pipelines. Familiarity with the AI agent security threat landscape: prompt injection, tool misuse, credential theft, and multi-turn escalation-style attacks.Preferred Qualifications Domain experience in healthcare AI (e.g., HIPAA/HITRUST), financial services AI (e.g., SOX, GLBA), or other regulated enterprise AI environments. Experience integrating AI agents with enterprise data systems (EHR/EMR, CRM, ERP) and defining production-ready tool schemas. Background in AI red-teaming, adversarial prompting, or LLM security research. Familiarity with regional enterprise or healthcare market dynamics relevant to key go-to-market partnerships.

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