Technology Specialist - IT Service, Support and Operations | Telecom | OSS/BSS

POC: Sam Chavez

ATTENTION ALL SUPPLIERS!!!

READ BEFORE SUBMITTING:
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MANDATORY: Please include in the resume the candidate s complete & updated contact information (Phone number, Email address and Skype ID) as well as a set of 5 interview timeslots over a 72-hour period after submitting the profile when the hiring managers could potentially reach to them. PROFILES WITHOUT THE REQUIRED DETAILS and TIME SLOTS will be REJECTED.

Job Title: Technology Specialist - IT Service, Support and Operations | Telecom | OSS/BSS
Work Location & Reporting Address: Richardson, TX 75082 (Onsite-Hybrid. Candidates willing to relocate to client location will be considered)
Contract duration: 12
MAX VENDOR RATE: #### to #### per hour max
Target Start Date: 15 Sep 2026
Does this position require Visa independent candidates only? Yes

Must Have Skills:
Python 3.11+
LangGraph
LangChain
Azure OpenAI/OpenAI APIs
FastAPI
Vector Databases
PostgreSQL
Docker

Nice to Have Skills:
Columnar performance tuning
Network operations domain knowledge
OAuth 2.0
Microsoft Graph API

Detailed Job Description:
Overall 8+ years experience with 5+ years in AI development. PFB the technology skills required.

Core Language & Architecture
Python 3.11+
Advanced type hints (PEP 484), static typing discipline
Async programming (asyncio, async/await, async generators)
aio / (async clients)
Pydantic v2 (BaseModel, validation, settings management)
Structured logging & tracing patterns
Redis (pub/sub, TTL, async clients)
REST API design & integration patterns
Retry/backoff strategies (Tenacity)
Concurrency patterns (parallel tool calls, task orchestration)

AI / LLM / Agent Systems
LangGraph (state machines, conditional edges, checkpointing)
LangChain 0.3.x (LLMChain, StructuredTool, retrievers, prompt templates)
ReAct-style agent architectures
Tool-based agent design (40+ tool environments)
Azure OpenAI / OpenAI APIs (GPT-4o, deployment mgmt, rate limits, token budgeting)
Prompt engineering (few-shot, structured output, JSON mode)
PydanticOutputParser / structured LLM responses
Guardrails / PII redaction patterns
Memory abstractions for agents
Langfuse (trace instrumentation, evaluation, prompt management)
LLM fallback chains & error recovery
RAG prompt grounding strategies
LLM fine-tuning
Neural Network training & tuning
Traditional ML models (random forest, k-means clustering, linear regression, etc.)
MCP development and consumption

Retrieval, Search & RAG Engineering
Vector databases (Qdrant and/or Milvus)
HNSW indexing parameters
Filtering strategies
Embedding pipelines (OpenAI ada-002 or equivalent)
Batch embedding & re-indexing workflows
Hybrid retrieval (BM25 + semantic)
Score fusion strategies
Cross-encoder reranking (BAAI/bge models)
FastAPI-based inference services
LangChain retriever abstractions
RAG evaluation metrics:
1. Faithfulness
2. Relevance
3. NDCG
4. MRR
Trace-level RAG evaluation (Langfuse)

Data Engineering & ETL
Prefect 2.x / 3.x
1. Flows, tasks, futures
2. Deployments (YAML)
3. Scheduling
ETL/ELT design o Schema evolution
1. Query optimization
OAuth authentication
Warehouse/schema management
PostgreSQL 16/17
1. psycopg 3.x
2. Connection pooling
3. SQLAlchemy 2.x (ORM + asyncio)
4. Alembic migrations
5. Advanced SQL
6. Multi-table JOINs
7. CTEs
8. Window functions
Timezone conversion
Pandas 2.x (complex multi-stage transformations)
PyArrow / columnar formats
Azure Blob Storage (azure-storage-blob)
Document ingestion/parsing:
1. Docling
2. Unstructured
3. python-docx
4. python-pptx

DevOps & Platform
Docker
Linux fundamentals

Nice-to-Haves
Ray (distributed execution)
Columnar performance tuning
Network operations domain knowledge
NOC / alarm correlation familiarity

API & Enterprise Integrations
OAuth 2.0 (client credentials flow, token lifecycle)
MSAL (browser + service principal flows)
Microsoft Graph API
SharePoint
Outlook
Planner
OneDrive
Pagination
App permissions
ServiceNow REST API
Table API
Incident/change mgmt
Bulk operations
Splunk SDK
Saved searches
Async queries
Log analysis
Azure AD app registrations
IPAM / OTNA integrations (nice-to-have domain exposure)

Minimum Years of Experience:
7+ years

Certifications Needed:
None

Top 3 responsibilities you would expect the Subcon to shoulder and execute:
RAG prompt grounding strategies LLM finetuning
API Enterprise Integrations OAuth 2.0 client credentials flow, token lifecycle MSAL browser service principal flows Microsoft Graph API SharePoint Outlook Planner OneDrive
Vector databases Qdrant andor Milvus

Interview Process (Is face to face required?)
Virtual

Any additional information you would like to share about the project specs/nature of work:

Project Code: Project code for Network Apps AI develop