AI Software Engineer, Agent Harness

Location: Bengaluru, Karnataka (or throughout India remote-friendly with travel)

About EnCharge AI

EnCharge AI is building the next generation AI platform. Our novel in-memory-computing architecture delivers a 10x step-function improvement in compute energy efficiency and performance for AI inference workloads. As the demands of artificial intelligence move beyond today's models, we believe fundamental underlying infrastructure must evolve. We are an experienced team of AI researchers, silicon & systems engineers, and architects backed by leading investors, poised to become the essential platform for the next wave of AI innovation.

The Opportunity

We serve open-weight models and our own bespoke checkpoints on EnCharge hardware. The models change often, and the harness around them needs to keep up. You own this layer that runs agents against files, tools, documents with permissions, memory, unattended execution, and real outputs. It will be assembled from a combination of open-source and bespoke code.

Key Responsibilities

Own the harness architecture end to end — agent loop, safe execution, context management, knowledge base, memory, permissions, orchestration, outputs, interfaces, observability — one component per layer, with clear interfaces so layers can be swapped.

Build the pieces with no open-source equivalent e.g. session semantics, enforced permissions, memory in a human-editable file, orchestrator, and outputs.

Keep pace with the models: adapters, prompt formats, tool-call schemas, stop conditions, benchmarking and evaluation.

Make tool use reliable across models of uneven tool-calling quality — validation, repair, retries, fallbacks.

Develop agents, tools, and MCP servers for internal and customer use cases, and review them for security before they ship.

Build the evaluation harness: task suites, regression runs on every model or harness change, cost and latency per task alongside quality.

Define the interfaces: session API, CLI, GUI, and an endpoint existing tools can point at.

Qualifications

10+ years of software engineering experience in backend systems or ML infrastructure

Strong Python and at least one systems language (e.g., Go, Rust, C++)

Have shipped and operated an agent loop in production — tool use, multi-step workflows, unattended runs

Hands-on with RAG, context management, and memory for LLM applications

Experience with sandboxing, isolation, and permission models for automated systems

Have run open-weight models yourself and understand how quantization and serving choices change model behavior

Comfort in fast-moving, ambiguous environments where you define the roadmap; strong product instincts

Nice to Have

Contributions to open-source agent harnesses or coding agents

Experience with agent benchmarks (e.g., SWE-bench, Terminal-Bench) and building internal task suites

MoE serving familiarity e.g. expert placement, tensor parallelism, quantization etc.

Observability for LLM systems

Document parsing and indexing pipelines

Desktop or GUI application experience