Senior AI Engineer

Milestone Technologies, Inc.

1 years

0 Lacs

india

Posted:1 day ago| Platform:

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Skills Required

ai data composer reasoning evaluation code retrieval latency json correlation sparql extraction encode routing model escalation test scoring metrics security checks content integration api elasticsearch azure openai vertex typescript rest design openapi opentelemetry profiling circuit management audit logging coding git capture testing regression strategies kubernetes service ux

Work Mode

On-site

Job Type

Full Time

Job Description

Senior AI Engineer

Key Responsibilities

Agent Implementation:
Build and harden
Intent
,
Planner
, and
Router/Composer
agents with typed JSON I/O, retries/timeouts, and idempotency; emit call-graph traces and correlation IDs.
Knowledge-Graph Reasoning:
Generate correct graph queries (
SPARQL/Gremlin/PGQL
) from planner outputs; perform subgraph extraction; encode rationale and references in responses.
RAG Baseline & Retrieval:
Implement document prep, chunking/embeddings, hybrid retrieval and (where available) reranking; maintain a high-quality baseline path for side-by-side comparisons.
Prompt/Config Tuning:
Version and tune prompts, routing policies (small→large model escalation), temperature/top-p settings, and caching; document routing outcomes and cost/latency budgets.
Evaluation Hooks:
Integrate test sets and scoring (faithfulness/correctness, precision/recall, multi-hop coverage, latency); enable
automated re-evaluation
on any change (model/agent/prompt/data).
Observability & Cost Controls:
Instrument traces/metrics/logs (token usage, latency P50/P95, error codes); surface cost-per-answer dashboards; implement backpressure and graceful degradation.
Security & Guardrails:
Enforce policy-as-code and entitlement checks (role/row/column), PII/PHI handling, content moderation, and HITL approval prompts for state-changing actions.
Quality & CI/CD:
Write unit/integration/contract tests; participate in PR reviews; ship via CI/CD with feature flags and environment promotion; maintain API/connector schemas and docs.

Required Skills

Applied LLM Engineering:
1-2+ years building production services; hands-on with LLM tool/function-calling, agent frameworks, and prompt/version management.
Knowledge & Retrieval:
Practical experience with
Knowledge Graphs
(RDF/SPARQL or property graph/Gremlin) and
RAG
pipelines (chunking, embeddings, retrieval/reranking).
Data/Model Ecosystem:
One or more vector DBs (pgvector, Pinecone, Weaviate, Milvus) and search (OpenSearch/Elasticsearch); familiarity with major model platforms (Azure OpenAI, Vertex, Anthropic, open-weights).
Backend Skills:
Proficiency in
Python
and/or
TypeScript/Node.js
; strong REST/gRPC API design, JSON Schema/OpenAPI, retries/backoff/idempotency, and error taxonomies.
Observability & Reliability:
OpenTelemetry (traces/metrics/logs), performance profiling, resiliency patterns (circuit breakers, bulkheads, DLQ/queues).
Security by Design:
OIDC/SSO, secrets management, least-privilege access, audit logging, and secure coding for AI/data services.
CI/CD & Testing:
Git-based workflows, automated pipelines, unit/integration/contract tests, and environment promotion practices.

Good to Have Skills

Ontology & Data Quality:
SHACL/OWL basics, ontology stewardship, lineage/provenance capture, and data quality checks for KG/RAG pipelines.
Evaluation Engineering:
Judge-model setups, A/B testing, rubric design, and regression dashboards.
Performance & FinOps:
Async I/O, caching strategies, connection pooling, and token/runtime budget enforcement.
Runtime & Platform:
Containers/Kubernetes, service mesh/API gateways, feature flags, blue/green or canary releases.
UX for Explainability:
Collaborating on rationale/explanations (source lists, subgraph summaries) and clear HITL approval prompts.

This role is ideal for a hands-on engineer who enjoys turning advanced reasoning patterns into robust, observable services-balancing quality, safety, and cost at enterprise scale.

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