Senior Software Engineer · AI Systems

Building reliable AI systems at production scale.

I design and own agentic platforms, evaluation and safety systems, and the backend infrastructure behind complex regulated workflows.

View selected work View experience
ENGINEERING OWNERSHIP PRODUCTION
01ArchitectureAgent runtimes & workflows
02ReliabilityTracing, evaluation & regression
03SafetyGuardrails, PII & red teaming
04ScaleBackend, search & data systems
DESIGN → OWN → OPERATE → IMPROVE

01 / SELECTED WORK

Systems built for everything after the model call.

Anonymized case studies focused on architecture, engineering decisions, reliability, and system-level outcomes.

01

Agent infrastructure

Production Agent Runtime

A modular runtime for evidence-heavy, regulated workflows—designed around orchestration, context assembly, skills, validation, and response generation rather than a single prompt chain.

  • Designed specialized flows across orchestrators, skills, validators, context, and response layers.
  • Separated shared runtime behavior from workflow-specific capabilities so the system can evolve without prompt-level coupling.
  • Built controlled transitions between reasoning, tool use, validation, and final response generation.

System outcome

Created an extensible foundation where new agent capabilities can be added without rebuilding the core loop.

  • Agent runtime
  • Orchestration
  • Skills
  • Validation
02

Evaluation, observability, and safety

Agent Reliability & Security Layer

A layered reliability system that makes agent behavior observable, regressions testable, and unsafe inputs detectable before they reach critical workflows.

  • Integrated Langfuse traces to inspect complete agent trajectories, tool calls, latency, and failure paths.
  • Built an internal evaluation suite for scenario-based quality checks and repeatable regression testing.
  • Combined PII masking and OpenAI guardrails with custom open-source classifiers for prompt injection, adversarial inputs, and red-team attack patterns.

System outcome

Moved reliability work from manual spot checks toward trace-driven diagnosis, measurable evaluations, and layered security controls.

  • Langfuse
  • Custom evals
  • PII masking
  • Red teaming
03

Context and retrieval intelligence

Context Quality Engine

A context-selection layer that treats retrieval as one input to the agent’s working state—not the whole intelligence system.

  • Combined multi-query expansion, hybrid retrieval, reciprocal rank fusion, and reranking.
  • Evaluated evidence coverage and failure modes across sparse, high-specificity domain corpora.
  • Shaped compact, task-specific context under strict token budgets and domain constraints.

System outcome

Materially improved evidence coverage over keyword-only retrieval while preserving inspectability and source grounding.

  • Hybrid search
  • RRF
  • Reranking
  • Context selection
04

Data systems

Scalable Analytics Data Layer

A shared enriched data model that replaces repeated high-cost joins and aggregations in interactive analytics workloads.

  • Designed full and differential refresh strategies for evolving source data.
  • Moved expensive cross-collection enrichment out of the request path.
  • Introduced explicit freshness semantics for downstream dashboards.

System outcome

Reduced repeated computation and created a more scalable path for analytics growth.

  • MongoDB
  • ETL
  • Materialized data
  • CubeJS

02 / ENGINEERING APPROACH

Reliable agents are engineered, not prompted.

01

Runtime before prompt

Treat the model as one component. Engineer orchestration, tools, state transitions, validators, and response contracts around it.

02

Context is a budget

Assemble the smallest high-signal state for each decision—evidence, instructions, history, and tool affordances—not one oversized prompt.

03

Evals start with traces

Use real trajectories to find failure modes, convert them into repeatable scenarios, and measure every harness change against regressions.

04

Security is layered

Protect boundaries with PII controls, policy guardrails, adversarial classifiers, and red-team scenarios instead of relying on one filter.

03 / EXPERIENCE

Progression from building features to owning systems.

JAN 2025 — PRESENT

Hyderabad, India

Senior Software Engineer

Regology India · AI systems & platform engineering

  • Design and own production agentic systems across orchestration, context, skills, validation, guardrails, and response layers.
  • Established reliability and safety foundations using Langfuse tracing, a custom evaluation suite, PII controls, adversarial detection, and red-team scenarios.
  • Drive architecture across AI, search, backend, and data systems while mentoring engineers and contributing to technical interviews.

JUL 2022 — JAN 2025

Hyderabad, India

Software Engineer

Regology India · Applied AI & backend engineering

  • Built retrieval, summarization, and regulatory-analysis workflows that became foundations for production AI capabilities.
  • Developed backend services and data pipelines across Python, Elasticsearch, and MongoDB for evidence-heavy enterprise workflows.
  • Progressed from feature delivery to end-to-end ownership of production reliability, performance, and system design.

04 / CAPABILITIES

Depth in agent systems. Breadth across the production stack.

01

Agent runtime & harness

The execution system around the model.

  • Agent orchestration
  • Context engineering
  • Harness engineering
  • Tool routing
  • Agent skills
  • Stateful workflows
  • Validators
02

Reliability & security

Evidence, controls, and feedback loops for production behavior.

  • Langfuse tracing
  • Custom evaluation suites
  • Regression testing
  • Guardrails
  • PII masking
  • Adversarial detection
  • Red-team testing
03

Context & search

High-signal evidence selection under finite attention.

  • Elasticsearch
  • Hybrid retrieval
  • Vector search
  • Multi-query expansion
  • Reciprocal rank fusion
  • Reranking
  • Source grounding
04

Backend, data & platform

The production foundations that keep AI systems useful.

  • Python
  • MongoDB
  • SQL
  • ETL pipelines
  • Materialized data models
  • AWS
  • Kubernetes
  • CI/CD

05 / RÉSUMÉ

Hari Krishna

Senior Software Engineer · Agentic AI systems

Profile

Senior Software Engineer building production agent runtimes across context, orchestration, evaluation, security, search, and data platforms.

Experience progression

Senior Software Engineer · Jan 2025 — Present
Software Engineer · Jul 2022 — Jan 2025

Education

BITS Pilani, Hyderabad Campus

Core focus

Agent runtime · Context engineering · Harness engineering · Reliability & security

This is a verified-content résumé snapshot. Contact details and the final résumé file will be added before public launch.

06 / CONTACT

Let’s build AI systems that hold up outside the demo.

Interested in senior software engineering opportunities focused on agent infrastructure, applied AI, reliability, and platform-scale problems.

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