AI Tech Lead
Jalasoft • Bolivia, Plurinational State of • Colombia
No Relocation
Posted: June 10, 2026
Job Description
Cross-Team Technical Coordination
- Serving as Scrum Master and Delivery Lead for both AI teams: organizing and facilitating sprint planning, daily stand-ups, backlog grooming, and retrospectives.
- Shielding both teams from day-to-day integration distractions by ensuring the junior development team receives clean task definitions, structured schemas, and clearly scoped technical requirements.
- Balancing high-speed AI prototyping demands against the structured pipeline stabilization cycles required for enterprise-grade development.
- Managing cross-team dependency and interface mapping to ensure smooth collaboration between the senior and junior engineering layers.
Architecture Translation & Gateway
- Translating strict architectural guardrails — network isolation, database connection limits, cost-containment — from the System Architects into practical workflows for the engineering teams.
- Partnering with Loftware Architects to ensure teams safely leverage AWS services and data read replicas without compromising corporate security boundaries, tenant isolation, or regional compliance.
- Leading technical review sessions to determine the appropriate storage strategy (Amazon MemoryDB / Redis OSS / Valkey vs. pgvector vs. OpenSearch), balancing developer needs against enterprise infrastructure standards.
AI & LLM Systems Quality Control
- Overseeing evaluation frameworks for multi-step agent workflows to ensure deterministic behavior and eliminate unhandled hallucinations.
- Validating that all data ingestion flows and internal tool-calling structures adhere to type-safe validation layers, preventing malformed agent responses from breaking downstream systems or leaking PII.
- Overseeing the centralized repository for system prompts, prompt caching strategies, and Amazon Bedrock configurations to ensure optimal performance, token budgeting, and corporate policy alignment.
Enterprise Deployment & Operational Stability
- Working with internal teams to define and enforce robust CI/CD strategies for AI agents, ensuring that changes to prompts, embeddings, or state-machine routing rules are deployed without service disruption.
- Contributing to operational protocols for deployment failures mid-workflow, ensuring both teams design for idempotency to handle unexpected model degradation or pipeline failures gracefully.
- 10+ years of experience in Software Engineering and/or Technical Leadership
- 3+ years leading AI/ML or high-throughput distributed systems teams
- Proven track record running agile methodologies (Scrum/Kanban) across multi-tiered or split engineering teams
- Deep hands-on architectural experience with LLMs and enterprise-scale systems
- Experience partnering with System Architects to govern AWS infrastructure usage, security controls, and resource provisioning
- Familiarity with agentic orchestration frameworks (LangGraph, AWS Step Functions, or equivalent) at an architectural governance level
- Working knowledge of Amazon Bedrock APIs, Guardrails, and Knowledge Base configurations
- Understanding of vector retrieval strategies (pgvector, Amazon OpenSearch/Elasticsearch) and in-memory data stores (Amazon MemoryDB / Redis OSS / Valkey)
- Experience designing for idempotency and stateful rollback in distributed AI pipelines
- Strong stakeholder management skills, with experience negotiating architectural and infrastructure decisions on behalf of engineering teams
- Hands-on implementation experience with Vercel AI SDK, LangGraph, or LlamaIndex
Additional Content
Cross-Team Technical Coordination
- Serving as Scrum Master and Delivery Lead for both AI teams: organizing and facilitating sprint planning, daily stand-ups, backlog grooming, and retrospectives.
- Shielding both teams from day-to-day integration distractions by ensuring the junior development team receives clean task definitions, structured schemas, and clearly scoped technical requirements.
- Balancing high-speed AI prototyping demands against the structured pipeline stabilization cycles required for enterprise-grade development.
- Managing cross-team dependency and interface mapping to ensure smooth collaboration between the senior and junior engineering layers.
Architecture Translation & Gateway
- Translating strict architectural guardrails — network isolation, database connection limits, cost-containment — from the System Architects into practical workflows for the engineering teams.
- Partnering with Loftware Architects to ensure teams safely leverage AWS services and data read replicas without compromising corporate security boundaries, tenant isolation, or regional compliance.
- Leading technical review sessions to determine the appropriate storage strategy (Amazon MemoryDB / Redis OSS / Valkey vs. pgvector vs. OpenSearch), balancing developer needs against enterprise infrastructure standards.
AI & LLM Systems Quality Control
- Overseeing evaluation frameworks for multi-step agent workflows to ensure deterministic behavior and eliminate unhandled hallucinations.
- Validating that all data ingestion flows and internal tool-calling structures adhere to type-safe validation layers, preventing malformed agent responses from breaking downstream systems or leaking PII.
- Overseeing the centralized repository for system prompts, prompt caching strategies, and Amazon Bedrock configurations to ensure optimal performance, token budgeting, and corporate policy alignment.
Enterprise Deployment & Operational Stability
- Working with internal teams to define and enforce robust CI/CD strategies for AI agents, ensuring that changes to prompts, embeddings, or state-machine routing rules are deployed without service disruption.
- Contributing to operational protocols for deployment failures mid-workflow, ensuring both teams design for idempotency to handle unexpected model degradation or pipeline failures gracefully.
- 10+ years of experience in Software Engineering and/or Technical Leadership
- 3+ years leading AI/ML or high-throughput distributed systems teams
- Proven track record running agile methodologies (Scrum/Kanban) across multi-tiered or split engineering teams
- Deep hands-on architectural experience with LLMs and enterprise-scale systems
- Experience partnering with System Architects to govern AWS infrastructure usage, security controls, and resource provisioning
- Familiarity with agentic orchestration frameworks (LangGraph, AWS Step Functions, or equivalent) at an architectural governance level
- Working knowledge of Amazon Bedrock APIs, Guardrails, and Knowledge Base configurations
- Understanding of vector retrieval strategies (pgvector, Amazon OpenSearch/Elasticsearch) and in-memory data stores (Amazon MemoryDB / Redis OSS / Valkey)
- Experience designing for idempotency and stateful rollback in distributed AI pipelines
- Strong stakeholder management skills, with experience negotiating architectural and infrastructure decisions on behalf of engineering teams
- Hands-on implementation experience with Vercel AI SDK, LangGraph, or LlamaIndex