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Job details:
About ETS Labs :
ETS Labs, an Etech Global Services Company, is a technology-
driven organization building enterprise-grade AI applications,
analytics platforms, and cloud solutions for global clients across
contact center, healthcare, and financial services domains. We are
at the forefront of AI-powered product engineering — delivering
conversational AI, agentic automation, RAG-based knowledge
platforms, and real-time analytics at scale.
Role Overview :
We are looking for a Senior AI Application Developer & Architect
who specializes in building production-grade AI-powered software
applications. The primary focus of this role is end-to-end
development of GenAI applications — including conversational AI
systems, agentic workflows, RAG pipelines, LLM-integrated APIs,
and real-time AI services. This is an application development role
where AI is the core product layer, not a research or model-training
position. The ideal candidate thinks like a software engineer first
and uses LLMs, agentic frameworks, and cloud AI services as the
primary building blocks.
Key Responsibilities :
AI Application Development (Primary Focus)
· Design and build end-to-end AI-powered applications —
conversational chatbots, agentic assistants, document intelligence
systems, and real-time AI analytics platforms.
· Develop LangGraph-based agentic workflows with multi-step
reasoning, tool orchestration, HITL approval gates, and crash-
recovery state persistence.
· Build and integrate LLM APIs (AWS Bedrock, OpenAI, Groq,
Gemini, Ollama) into production application backends with
pluggable provider abstraction.
· Develop real-time AI features using FastAPI and WebSocket
streaming, delivering token-by-token LLM responses to end users.
· Build Text-to-SQL engines, automated data visualization
pipelines, and AI-driven analytics features within application layers.
· Integrate multimodal AI capabilities — OCR, document
parsing, image understanding — into application workflows where
required.
RAG & Knowledge Retrieval Systems :
· Build production-grade RAG pipelines integrating vector
databases (Weaviate, Pinecone, OpenSearch) with hybrid dense +
BM25 retrieval.
· Implement query transformation, reranking (Cohere,
CrossEncoder), and LLM-based citation validation within application
flows.
· Design multi-tenant document ingestion pipelines with per-
user isolation, lifecycle tracking, and scheduled processing.
· Develop knowledge base chatbot applications with multi-turn
conversational memory and sliding window context compaction.
Team Leadership & Solution Architecture :
· Lead a small team of 2–3 AI developers, conducting code
reviews, architecture walkthroughs, and delivery planning.
· Create solution architecture diagrams covering application,
integration, data flow, cloud, and security layers.
· Act as the technical owner for AI application delivery — from
requirements to production deployment.
· Translate business requirements and client use cases into AI
application designs and implementation roadmaps.
Backend Engineering & Cloud Deployment :
· Build scalable backend services in Python (FastAPI) with
async concurrency, task queuing (Celery + Redis), and scheduled
processing (APScheduler).
· Implement RBAC systems, multi-tenant data isolation, and API
security patterns within AI application architectures.
· Deploy AI applications on AWS (ECS Fargate, Lambda) via
CI/CD pipelines with container orchestration and secrets
management.
· Apply performance engineering practices: async circuit
breakers, retry logic, connection pooling, and memory optimization
for production AI workloads.
Responsible AI & Quality :
· Implement AI guardrails, prompt injection prevention, output
validation, and content moderation within application pipelines.
· Build PII detection, audit logging, traceability, and
explainability features for compliance-sensitive AI applications.
· Write unit and integration tests for AI application components,
ensuring reliability of LLM-integrated workflows.
Required Skills & Expertise
AI Application Development (Core — Must Have)
· LangGraph, LangChain — agentic workflow design, state
machines, conditional routing, tool nodes
· LLM API integration — AWS Bedrock (Claude, Llama, Mistral,
Titan), OpenAI GPT-4o, Gemini, Groq, Ollama
· Prompt Engineering — structured prompts, output formatting,
few-shot design, chain-of-thought reasoning
· RAG pipeline development — document ingestion, chunking,
embedding, hybrid retrieval, reranking, generation
· Conversational AI — multi-turn memory, session
management, context compaction, streaming responses
· HITL workflow design — approval gates, escalation flows,
human override mechanisms
Backend & API Development
· Python — FastAPI, asyncio, REST API design, WebSocket
streaming
· Task queuing — Celery, Redis; Scheduling — APScheduler
· Databases — MongoDB, PostgreSQL, SQLite for application
data and lifecycle tracking
· Authentication & Authorization — JWT, RBAC, OAuth, multi-
tenant patterns
Vector Databases & Search
· Weaviate (multi-tenant), Pinecone (namespace isolation),
OpenSearch — production deployment experience
· Hybrid retrieval: BM25 + dense vector, reranking with
CrossEncoder or Cohere
· Embedding models: Amazon Titan Embed v2, OpenAI Ada,
local sentence transformers
Cloud & DevOps
· AWS — Bedrock, ECS (Fargate), Lambda, S3, Secrets
Manager, CloudWatch
· Docker, Kubernetes basics, GitLab / GitHub CI/CD pipelines
· Infrastructure as Code awareness (Terraform /
CloudFormation) is a plus
ML & NLP Awareness (Good to Have — Not Primary)
· Basic understanding of NLP concepts: tokenization,
embeddings, text classification — sufficient to work with pre-trained
models via APIs.
· Familiarity with Hugging Face model hub for accessing pre-
trained models (BERT, sentence transformers) when needed in
application pipelines.
· Understanding of when to use fine-tuned models vs. prompt
engineering vs. RAG — to make the right architectural choice.
· Experience with traditional ML frameworks (TensorFlow, scikit-
learn) is a plus but not required for this role.
Responsible AI
· AI guardrails, output validation, prompt injection prevention,
and content moderation
· PII detection, compliance monitoring, audit trails, and
traceability in AI application outputs
· Multi-tenant data isolation and security-aware AI application
design
Preferred Qualifications
· B.Tech / M.Tech / BCA / MCA in Computer Science, Software
Engineering, AI/ML, or equivalent.
· 5+ years of software development experience with at least 3
years in AI application development.
· Proven track record of delivering 3+ production AI applications
(chatbots, agentic systems, RAG platforms).
· Demonstrated experience with LangGraph or similar agentic
orchestration frameworks.
· Exposure to contact center AI, document intelligence, or
enterprise analytics AI platforms.
· Strong API design, code quality, and software engineering
fundamentals.
· AWS certifications (Developer, Solutions Architect) are a plus;
ML Specialty not required.
Nice to Have
· Experience with MCP (Model Context Protocol) server
development and tool orchestration.
· Voice bot or speech-to-text/speech translation pipeline
development.
· Exposure to multimodal AI: OCR, document parsing, image
understanding within application workflows.
· Knowledge of FedRAMP, HIPAA, SOC 2, or regulated-industry
AI compliance requirements.
· Experience with streaming front-end integration — React or Angular consuming WebSocket AI responses.
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