Job Description VN * Chu trch nhim xy dng v thit lp chin lc cng Gim c Cng ngh (CTO), h tr v t vn cho Ban Gim c (BOD) xy dng l trnh AI ca cng ty, pht trin tm nhn, chin lc v k hoch nng lc v AI v khoa hc d liu. * Pht trin v dn dt i ng ng dng (applied team) v i ng Nghin cu & Pht trin (R&D) nghin cu v cung cp cc gii php AI, khoa hc d liu, hc my (machine learning) v hc su (deep learning). * ng vai tr chuyn gia k thut ct li, trc tip kin trc v xy dng cc gii php c kh nng m rng, ng thi thc y s xut sc v d liu trn ton t chc. * Thit k ton b Nn tng AI/ML (AI/ML Platform) v xy dng h tng h tr kh nng m rng. * Cung cp vai tr lnh o hp tc xy dng chc nng AI, cc k s hc my (MLEs), k s MLOps v k s d liu (Data Engineering). * Hp tc vi cc bn lin quan v qun l d n chuyn i mc tiu kinh doanh thnh gii php k thut. * Xc nh cc phng php tt nht da trn AI/ML v vng i AI. Gim st v h tr cc thnh vin trong vic hun luyn v ti u ha m hnh. * Hun luyn v c vn cho i ng pht trin khi xy dng cc gii php d liu v AI/hc my c kh nng m rng. m bo cc thnh vin trong nhm nm r cc tiu chun hiu sut mong i, c thc y v pht trin cung cp cc dch v hiu qu. * Xy dng v trin khai cc m hnh AI/ML u-cui (end-to-end) v cc gii php ng dng AI/ML, tn dng cc ch s (metrics) h tr cc d on, xut, tm kim v chin lc tng trng. * a ra thng tin v thc hin cc l trnh sn phm y tham vng thc y tng trng.
Experience Required VN 1/ Hc vn * Bng c nhn v k thut, khoa hc my tnh hoc cc lnh vc nh lng lin quan.
2/ K nng cng vic K nng nghin cu, kh nng phn tch, th mnh gii quyt vn , k nng giao tip, nng lc my tnh, th mnh t chc, k nng qun l thi gian v xy dng i nhm.
3/ K nng yu cu * C hn 5 nm kinh nghim thc t trong lnh vc AI/ML, thit k v trin khai cc m hnh AI v hc my. * Kinh nghim su rng trong vic thit k, pht trin, trin khai v qun l cc thut ton, m hnh v phng php qun l m hnh. * Kinh nghim ng k trong vic tuyn dng, thnh lp v qun l i ng khoa hc d liu/AI. * Kinh nghim xc nh v lnh o cc d n quy m ln vi nhiu bn lin quan. * Nn tng vng chc v pht trin phn mm, c kinh nghim trong vic xy dng h thng phn mm * Kin thc cp nht v cc cng ngh AI/ML v xu hng ca chng, bao gm cc th vin v cng c khc nhau. * Chuyn mn vi cc cng ngh tin tin nh hc chuyn giao (transfer learning), sinh c trng khng gim st (unsupervised feature generation), siu hc (meta-learning), m hnh vn bn to sinh (generative text models), th gic my tnh (computer vision), hp nht cm bin (sensor fusion), hoc hc tng cng (reinforcement learning). * xy dng cc kho c trng (feature stores) v nng lc AutoML. * Kin thc v cch thc hot ng, kh nng m rng v hiu sut ca cc sn phm SaaS ton din (full stack). * Kinh nghim trong vic xy dng kin trc trn h tng m my (AWS, Azure, GCP...). * Kinh nghim vi cc c s d liu hin i, mi trng m my v h sinh thi d liu. * K nng ton hc v khoa hc d liu nng cao (v d: bng Tin s v m hnh ha tnh ton, hc my, thng k, khoa hc my tnh) (Ty chn). * K nng giao tip v thuyt trnh bng vn bn v li ni xut sc, bao gm kh nng gii thch cc vn k thut cho i tng khng chuyn ngnh ( C-level, manager, ngi dng). * Kin thc v cc quy trnh v thc tin nh gi ri ro v mi e da.
4/ K nng k thut * Thnh tho cc thut ton AI/ML v c kh nng nghin cu cc bi bo mi, p dng v trin khai cc thut ton ca chng. * S dng thnh tho nhiu cng c AI v ngn ng nh Python, C/C++, R... cc framework hc my nh Spark, TensorFlow, hoc scikit-learn. C kinh nghim trong pht trin v trin khai trn Edge Device vi cu hnh thp/trung bnh * Thit k v pht trin cc gii php AI: X l nh (Image processing), X l ngn ng t nhin (NLP), H thng gi (Recommendation), AI to sinh (Gen AI) (LLM/LVM...). * C kh nng gii quyt cc vn phc tp trong lnh vc cng ngh gio dc (ed-tech) bng ML/DL. * K nng phn tch, trnh by d liu v trc quan ha d liu xut sc. * K nng k thut xut sc trong lnh vc/ngnh lin quan
