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Agent Application Engineer (Backend / Agentic AI) — Level 1
Agent Application Engineer (Backend / Agentic AI) — Level 1

Agent Application Engineer (Backend / Agentic AI) — Level 1 at Sentiaflow · Remote, Bengaluru (Bangalore), Pune, Delhi, Gurugram, Noida · 3 - 8 years · ₹25L - ₹40L / yr · Bootstrapped · Remote friendly · Posted 13 Sep 2026

Sentiaflow's logo

Agent Application Engineer (Backend / Agentic AI) — Level 1

Sonal Agarwal's profile picture
Posted by Sonal Agarwal
3 - 8 yrs
₹25L - ₹40L / yr
Remote, Bengaluru (Bangalore), Pune, Delhi, Gurugram, Noida
Skills
LangGraph
Web API
Information architecture
knowledge Graph
Semantic search

About Sentiaflow

Sentiaflow is an AI engineering and IT services company building production-grade agentic AI systems, sitting at the intersection of LLMs and real business operations — data, APIs, permissions, workflow state, human decisions, security, and measurable outcomes. Our initial domain focus is healthcare, particularly clinical trials, where reliability, traceability, and clear human-decision boundaries matter more than a slick demo.

Job Description

As a Level 1 engineer, you'll implement bounded parts of a production agentic workflow under an Agent Captain or senior engineer, connecting models to application services, tools, data sources, and human approval points.

You will:

  • Translate a scoped business workflow into typed inputs, outputs, states, actions, and escalation paths
  • Build backend services and tool integrations (Node.js/TypeScript or Python)
  • Use LLMs only where model judgment adds value; keep rules, validation, authorization, and workflow control in deterministic code
  • Design and validate structured model outputs before they touch downstream systems
  • Handle partial data, tool failures, duplicate events, retries, timeouts, rate limits
  • Add logging, traces, metrics, and decision records for diagnosability
  • Write tests and evaluation cases that check whether the full workflow behaves correctly — not just whether output sounds fluent
  • Protect sensitive data; participate in code, design, and release-readiness reviews
  • Explain implementation trade-offs clearly to engineers and stakeholders

Success in 6 months: own a bounded workflow module end-to-end, integrate models without letting probabilistic output bypass deterministic controls, produce release-ready evaluation evidence, and diagnose cross-boundary failures with less supervision.

Desired Skills

  • Approximately 3–6 years hands-on backend/application engineering experience, with demonstrable hands-on work building agentic systems — not just calling an LLM API from a backend service
  • LangGraph (or comparable agent orchestration framework) experience is required — building multi-step, stateful agent workflows with conditional branching, tool-calling loops, and recovery/retry logic, not a single-prompt wrapper
  • Deep RAG experience, including:
  • Chunking strategy design, embedding model selection, and retrieval evaluation (not just "connected a vector DB")
  • Hybrid search (dense + sparse/keyword), re-ranking, and query rewriting/decomposition
  • Handling retrieval failure modes: irrelevant context, stale data, contradictory sources, citation/grounding accuracy
  • Measuring RAG quality (precision/recall on retrieval, faithfulness/groundedness of generation) — not eyeballing outputs
  • Experience designing agent state machines / workflow graphs: tool selection, planning loops, human-in-the-loop interrupts, checkpointing, and state persistence across long-running workflows
  • Strong programming in JS/TypeScript (preferred), Python, Java, C#, or Go
  • Solid grasp of API design, databases, async processing, auth, testing, deployment
  • Comfort reasoning about state, retries, idempotency, concurrency, permissions, audit trails, failure recovery
  • Real production debugging experience, not just greenfield builds
  • Clear technical communication

We're looking for engineers who've actually built and tuned agentic/RAG systems in production — not those who've only wired together frameworks or prompted an LLM API.


Nice to have: experience with other orchestration frameworks (CrewAI, AutoGen, custom state machines), observability/eval tooling (LangSmith, Langfuse, custom trace pipelines), healthcare or regulated-industry background. Bachelors from IIT or NIT highly preferred.

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About Sentiaflow

Founded :
2012
Type :
Products & Services
Size :
0-20
Stage :
Bootstrapped

About

Sentiaflow is an AI engineering company headquartered in New Delhi, building production-grade AI systems for clients around the world — including Fortune 500 enterprises alongside high-growth startups. We work across finance, healthcare, and technology, helping organizations move beyond AI experimentation into real, reliable, production deployments.


We operate in three ways: embedding dedicated AI engineers directly into client teams, building custom AI solutions end-to-end — including RAG pipelines, LLM integrations, and AI agents — and designing the MLOps infrastructure that keeps these systems running reliably at scale.


Joining Sentiaflow means working on live, client-facing AI systems from day one — not internal prototypes shelved after a demo. Our engineers ship production code for organizations that depend on it, across some of the most demanding industries for AI reliability and compliance.


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Tech stack

skill iconPython
skill iconNodeJS (Node.js)
duckdb
skill iconPostgreSQL
LangGraph
OpenAISDK

Candid answers by the company

What is the location preference of jobs?
Why should I join Sentiaflow ?

Delhi-NCR, Pune or Bangalore

Company social profiles

N/A

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https://app.justinterview.ai/apply/10004915?src=cutshort


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Node.js, TypeScript, REST, GraphQL, PostgreSQL, SQL, Prisma / TypeORM / Sequelize, AWS (ECS, EC2, S3, RDS, Lambda), Airbyte / dbt / Airflow / AWS Glue, Data Warehousing, Event-Driven Architecture, Message Queues, CI/CD, Automated Testing, Backend Architecture, Scalability & Performance.


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We are hiring a Backend Engineer to build and operate the services that power a leading technology marketplace being relaunched under new ownership.

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·       Proxy control plane — Build the management APIs and background workers that configure the runtime proxy: API keys, plans, rate-limit policies, quota caches, abuse signals.

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•    Solid API design — REST and GraphQL (ideally federation: Apollo Federation, Mercurius, or similar). Schema design, persisted queries, performance tuning.

•    Production fluency with SQL — MySQL and/or Postgres. Bonus for Redis or ClickHouse.

•    Experience with event-driven systems — Kafka or similar. You understand idempotency, exactly-once myths, and consumer lag.

•    Hands-on AWS (EKS, MSK, Kinesis, DynamoDB, Lambda, DMS) and Kubernetes. Comfortable reading and writing Terraform.

•    Modern engineering practice: observability, feature flags, CI/CD, and safe rollouts.

•    Care for correctness on the money path — you have shipped billing, metering, or payment code before and have the scars to show for it.

•    Bonus: experience with API gateways, rate limiting, quota enforcement, usage metering, agent protocols (MCP, A2A, x402), or LLM-based developer tooling.

•    You write small, reviewable PRs. You add the test before you fix the bug. You would rather ship a smaller thing well than a bigger thing badly.

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•Senior individual contributor owning backend services for a large-scale enterprise SaaS platform (Kanban-style work management and flow).

•Designs and ships features end-to-end, collaborating closely with frontend, product, and QA.

What you’ll do

•Design, implement, and maintain Node.js (TypeScript/JavaScript) backend services and APIs.

•Model and evolve core domain entities (boards, lanes, work items, WIP rules, permissions, cross-team connections).

•Build secure, multi-tenant REST/JSON APIs consumed by web frontends and other internal services.

•Implement business logic for work-flow, metrics, and cross-team visibility.

•Improve performance, reliability, and observability of existing services.

•Integrate backend services with data storage and analytics layers via well-designed interfaces.

•Write and maintain automated tests (unit, integration, contract) and participate in code reviews.

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Tech stack

•Node.js, TypeScript/JavaScript

•REST/JSON APIs (GraphQL familiarity a plus)

•Relational/NoSQL data stores

•CI/CD, logging, metrics, and alerting tools


Experience & qualifications

•~6–9 years of backend engineering experience building web or SaaS products.

•Strong Node.js skills and experience designing clean API boundaries.

•Solid understanding of data structures, algorithms, and distributed system basics.

•Experience in agile teams; comfortable collaborating across time zones.


Nice to have

•Experience with Kanban/Agile tools or other developer/productivity SaaS products.

•Experience using AI-assisted coding tools (e.g., GitHub Copilot, Claude Code) in day-to-day development.

•Hands-on experience with React or close collaboration with React frontend teams.

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1 candid answer
1 video
Daniel Castellanos
Posted by Daniel Castellanos
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Most sales tools help you send emails. We’re building something different.


At Salesforge, we’re creating autonomous AI agents that can:


Find the right prospects

Generate highly personalized outreach

Run conversations

And book meetings


All without human involvement.


Why this is interesting


A lot of AI products stop at “generate text.” We’re focused on outcomes.


That means solving problems like:


How do you generate messages that actually get replies?

How do you evaluate and improve agent performance over time?

How do you orchestrate millions of AI-driven interactions reliably?

How do you combine structured data + LLMs in a way that scales?


If you enjoy working at the intersection of systems + AI + real-world feedback loops, this will feel like a playground.


What you’ll be working on


You won’t be maintaining legacy systems.


You’ll be:


Designing and building core backend systems that power our AI agents

Creating APIs and services that handle high-scale, real-time workflows

Working with queues (Kafka / SQS / RabbitMQ) to orchestrate async systems

Thinking deeply about performance, cost, and reliability in AI pipelines

Shipping features end-to-end with a small, senior team


The team


We’re a small group of experienced builders. We move quickly, care about quality, and avoid unnecessary process.


No layers of management.

No long planning cycles.

Lots of ownership and autonomy.


What we’re looking for


5+ years of backend engineering experience

Strong system design fundamentals

Experience with distributed systems and async processing

Familiarity with relational and/or document databases

Clear communicator, low ego, high ownership


Why join


You’ll work on a product where the output is measurable (meetings booked, revenue generated)

You’ll have real ownership from day one

You’ll be early in building a new category (AI sales agents)

You’ll grow as fast as we do

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Agentic Universe
Aryamann N
Posted by Aryamann N
Bengaluru (Bangalore)
3 - 7 yrs
₹25L - ₹35L / yr
Databases
Failover
Infrastructure management
skill iconPython
Artificial Intelligence (AI)
+1 more


Why this role exists 

Most systems work at a low scale. 

Very few survive real production load, complex workflows, and enterprise edge cases. 

We are building a platform that must: 

  1. Scale from 500K → 20M+ interactions/month 
  2. Handle complex insurance workflows reliably 
  3. Become easier to deploy as it grows, not harder 
  4.  
  5. This role exists to build the backend foundation that makes this possible. 

 

What you’ll do 

You will not just write services. 

You will design and own core platform systems. 

 

1. Scale the platform without breaking architecture 

  1. Scale from 50K → 4M+ interactions/month 
  2. Ensure: 
  3. High availability 
  4. Low latency 
  5. Fault tolerance 
  6. Avoid large rewrites — build systems that evolve cleanly 

 

2. Build the workflow automation (WA) engine 

  1. Design a flexible system with: 
  2. States 
  3. Stages 
  4. Cohorts 
  5. Dynamic workflows 
  6. Ensure workflows: 
  7. Handle edge cases reliably 
  8. Can be configured easily 
  9. Move from: 
  10. Hardcoded flows → configurable execution engine 

 

3. Build the insurance-specific data layer 

  1. Design data models for: 
  2. Policy states 
  3. Claim workflows 
  4. Consent tracking 
  5. Ensure the system works across: 
  6. Multiple insurers 
  7. Multiple use cases 
  8. Build a platform-first data layer, not use-case-specific hacks 

 

4. Make deployment and setup simple 

  1. Ensure workflows and data models are: 
  2. Easy to configure 
  3. Easy to launch 
  4. Reduce friction for: 
  5. Product teams 
  6. Deployment teams 

 

5. Create a compounding data advantage 

  1. Build a data layer that: 
  2. Improves with every deployment 
  3. Captures structured signals 
  4. Ensure data becomes a long-term edge, not just storage 

 

6. Own production reliability 

  1. Participate in on-call rotation across 3 engineers 
  2. Ensure: 
  3. Incidents are handled quickly 
  4. Root causes are fixed permanently 
  5. Build systems where reliability is shared, not individual 

 

What success looks like 

  1. Platform scales to 2M+ interactions/month smoothly 
  2. Workflow engine supports complex, dynamic use cases 
  3. Data layer enables fast deployment across accounts 
  4. Edge cases are handled without constant firefighting 
  5. System becomes easier to use as it grows 
  6. Production issues are rare and predictable 

 

Who you are 

 

  1. You have built: 
  2. Scalable systems 
  3. Distributed services 
  4. You think in: 
  5. Systems 
  6. Data models 
  7. Trade-offs 
  8. You are comfortable owning: 
  9. Architecture 
  10. Production systems 

 

What will make you stand out 

  1. Experience building: 
  2. Workflow engines 
  3. State machines 
  4. Data-heavy platforms 
  5. Strong understanding of: 
  6. System design 
  7. Distributed systems 
  8. Failure handling 
  9. Experience working in: 
  10. High-scale production environments 

 

Why join 

  1. You will build the core backend of an AI platform 
  2. Your work directly impacts: 
  3. Scale 
  4. Reliability 
  5. Product capability 
  6. You will design systems that move from: 
  7. Use-case specific → platform-level infrastructure 

 

What this role is not 

  1. Not just API development 
  2. Not limited to feature-level work 
  3. Not disconnected from production realities 

 

What this role is 

  1. A system architect 
  2. A builder of scalable platforms 
  3. A driver of long-term technical advantage 


One question to self-evaluate 

Can you design backend systems that scale, handle edge cases, and become easier to use as they grow? 

 

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